ABBYY Compreno is a natural language processing technology that enables knowledge workers to extract insights and intelligence from unstructured text, transforming Dark Data into useful, actionable information.
Try Compreno for free https://www.abbyy.com/compreno/
Intelligent Text Analytics with ABBYY ComprenoABBYY
Learn how Compreno's text analytics technology understands text meaning based on this language representation and analyzes content to detect key textual elements and the relationships between them
Try Compreno now https://www.abbyy.com/compreno/
The Nuts and Bolts of Metadata Tagging and Taxonomies Made Easy WebinarConcept Searching, Inc
Taxonomies are often thought of as hard to use and needing specialized applications or IT skills. Not so with Concept Searching’s unique technologies.
Join Michael Paye, our CTO, to see how taxonomies, auto-classification, and multi-term metadata generation unburden the IT team, eliminate end user tagging, and empower business users.
Understand the return on investment from an effective infrastructure solution for search, security, compliance, eDiscovery, records management, knowledge management, collaboration, and migration activities.
• Learn how our solution can meet either one challenge or several, and see how it works with different applications
• Watch multi-term metadata being automatically generated
• See how easy it is to use unique taxonomy tools and interactive features, such as clue suggestion, instant feedback, and assigning weights to terms
• Discover the value of dynamic screen updating to immediately see the impact of taxonomy changes
• View how document movement feedback enables you to see the cause and effect of changes without re-indexing
Data science with python certification training course withkiruthikab6
Python full coding from scratch
Visualization with Python
Statistics - theory and application in business
Machine Learning with Python - 6 different algorithms
Multiple Linear regression
Logistic regression
Variable Reduction Technique - Information Value
Forecasting - ARIMA
This slide was used in ISO/IEC JTC1 SC36 Plenary Meeting in June 22, 2015.
Title of this slide is 'Proof of Concept for Learning Analytics Interoperability and subtitle is 'Reference Model based on open source SW'.
Coexist or Integrate? Manage Unstructured Content from Diverse Repositories a...Concept Searching, Inc
Are you successfully managing your unstructured content? Have you quantified the risks and costs of not proactively managing your content? Did you know that you can dramatically improve search, eDiscovery, security, records management, migration, collaboration, text analytics, and business social applications, just by getting your unstructured content in order? Learn how to effectively clean up, optimize, and organize your file share content.
There are key solutions built on core technology platforms that will enable you to achieve these improvements. The conceptClassifier for SharePoint and conceptClassifier for Office 365 platforms automatically generate multi-term metadata that form concepts. Imagine it – eliminating end user tagging.
And the conceptClassifier for File Shares utility makes file shares discoverable, searchable, optimized, and organized. It automatically tags and classifies documents to a term set, for improving search and eDiscovery, and preparing content for migration.
Auto-classification and one natively integrated taxonomy/Term Store, available on-premises, in the cloud, or in a hybrid environment, provide the backdrop for a single enterprise search, regardless of where end users are located. Tackle information governance and standardize processes across the entire enterprise.The team from C/D/H provided the knowledge, planning, and optimization to intelligently migrate the manufacturer’s content from on-premises Search 2013 to the Office 365 Hybrid Search platform, using Concept Searching’s new utility, conceptClassifier for Hybrid Search.
The solution allows any of the 40,000 users to search 20 million documents from over 30 content sources, securely and within seconds. It leveraged the Microsoft Azure cloud platform, which reduced the required infrastructure tenfold, while improving performance and reducing complexity in the digital workplace.
Steve Mann will be joined by Steve Smith, Consultant from strategic partner C/D/H
Join Concept Searching and partner C/D/H for this thought-provoking webinar on what intelligent enterprise search should be.
Our solution is unique in the marketplace, and overcomes the limitations of other enterprise search engines. It was originally deployed as an enterprise search solution for engineers and support staff.
This webinar will focus on how one unified view of all unstructured, semi-structured, and structured data assets, including 2D and 3D images, can be integrated into the search interface, with previewers and navigational aids.
Both business and technical professionals will benefit from this session:
• Understand how the technology works, and how it can be set up with a platform and search engine of choice
• See how search returns results, and provides visual and navigational aids for all information retrieved
• Watch how to select an image based on color, size, or shape
• Learn how any business or artificial intelligence applications can benefit from the multi-term metadata created
• Find out why the search framework provides a responsive user interface for any tablet, PC or mobile device
Metadata used to be an afterthought. Now, metadata is a pre-requisite and the optimal mechanism to drive business processes like security and records and, of course, to manage content.
In this session Robert Piddocke, our Vice President of Channel and Business Development – passionate about information management, and author of books on SharePoint Search – explores how going meta helps transcend typical metadata use.
Robert discusses SharePoint functionality and what needs to be put in place to deploy a metadata-driven enterprise and build a framework for the future, and how metadata can be used to automate and drive business processes, and proactively manage content.
Speaker:
Robert Piddocke – Vice President of Channel and Business Development at Concept Searching
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
Watch video presentation and get a FREE performance management kit at
http://www.lifecycle-performance-pros.com
This presentation takes you through the steps of understanding your business intelligence needs and identifying the right tools for you. We discuss the different types of BI tools. We to discuss the criteria for selecting each type of tools. We to discuss popular Business Intelligence vendors and how to rate them. And we are going to discuss the job functions and responsibilities for a typical BI implementation
Intelligent Text Analytics with ABBYY ComprenoABBYY
Learn how Compreno's text analytics technology understands text meaning based on this language representation and analyzes content to detect key textual elements and the relationships between them
Try Compreno now https://www.abbyy.com/compreno/
The Nuts and Bolts of Metadata Tagging and Taxonomies Made Easy WebinarConcept Searching, Inc
Taxonomies are often thought of as hard to use and needing specialized applications or IT skills. Not so with Concept Searching’s unique technologies.
Join Michael Paye, our CTO, to see how taxonomies, auto-classification, and multi-term metadata generation unburden the IT team, eliminate end user tagging, and empower business users.
Understand the return on investment from an effective infrastructure solution for search, security, compliance, eDiscovery, records management, knowledge management, collaboration, and migration activities.
• Learn how our solution can meet either one challenge or several, and see how it works with different applications
• Watch multi-term metadata being automatically generated
• See how easy it is to use unique taxonomy tools and interactive features, such as clue suggestion, instant feedback, and assigning weights to terms
• Discover the value of dynamic screen updating to immediately see the impact of taxonomy changes
• View how document movement feedback enables you to see the cause and effect of changes without re-indexing
Data science with python certification training course withkiruthikab6
Python full coding from scratch
Visualization with Python
Statistics - theory and application in business
Machine Learning with Python - 6 different algorithms
Multiple Linear regression
Logistic regression
Variable Reduction Technique - Information Value
Forecasting - ARIMA
This slide was used in ISO/IEC JTC1 SC36 Plenary Meeting in June 22, 2015.
Title of this slide is 'Proof of Concept for Learning Analytics Interoperability and subtitle is 'Reference Model based on open source SW'.
Coexist or Integrate? Manage Unstructured Content from Diverse Repositories a...Concept Searching, Inc
Are you successfully managing your unstructured content? Have you quantified the risks and costs of not proactively managing your content? Did you know that you can dramatically improve search, eDiscovery, security, records management, migration, collaboration, text analytics, and business social applications, just by getting your unstructured content in order? Learn how to effectively clean up, optimize, and organize your file share content.
There are key solutions built on core technology platforms that will enable you to achieve these improvements. The conceptClassifier for SharePoint and conceptClassifier for Office 365 platforms automatically generate multi-term metadata that form concepts. Imagine it – eliminating end user tagging.
And the conceptClassifier for File Shares utility makes file shares discoverable, searchable, optimized, and organized. It automatically tags and classifies documents to a term set, for improving search and eDiscovery, and preparing content for migration.
Auto-classification and one natively integrated taxonomy/Term Store, available on-premises, in the cloud, or in a hybrid environment, provide the backdrop for a single enterprise search, regardless of where end users are located. Tackle information governance and standardize processes across the entire enterprise.The team from C/D/H provided the knowledge, planning, and optimization to intelligently migrate the manufacturer’s content from on-premises Search 2013 to the Office 365 Hybrid Search platform, using Concept Searching’s new utility, conceptClassifier for Hybrid Search.
The solution allows any of the 40,000 users to search 20 million documents from over 30 content sources, securely and within seconds. It leveraged the Microsoft Azure cloud platform, which reduced the required infrastructure tenfold, while improving performance and reducing complexity in the digital workplace.
Steve Mann will be joined by Steve Smith, Consultant from strategic partner C/D/H
Join Concept Searching and partner C/D/H for this thought-provoking webinar on what intelligent enterprise search should be.
Our solution is unique in the marketplace, and overcomes the limitations of other enterprise search engines. It was originally deployed as an enterprise search solution for engineers and support staff.
This webinar will focus on how one unified view of all unstructured, semi-structured, and structured data assets, including 2D and 3D images, can be integrated into the search interface, with previewers and navigational aids.
Both business and technical professionals will benefit from this session:
• Understand how the technology works, and how it can be set up with a platform and search engine of choice
• See how search returns results, and provides visual and navigational aids for all information retrieved
• Watch how to select an image based on color, size, or shape
• Learn how any business or artificial intelligence applications can benefit from the multi-term metadata created
• Find out why the search framework provides a responsive user interface for any tablet, PC or mobile device
Metadata used to be an afterthought. Now, metadata is a pre-requisite and the optimal mechanism to drive business processes like security and records and, of course, to manage content.
In this session Robert Piddocke, our Vice President of Channel and Business Development – passionate about information management, and author of books on SharePoint Search – explores how going meta helps transcend typical metadata use.
Robert discusses SharePoint functionality and what needs to be put in place to deploy a metadata-driven enterprise and build a framework for the future, and how metadata can be used to automate and drive business processes, and proactively manage content.
Speaker:
Robert Piddocke – Vice President of Channel and Business Development at Concept Searching
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
Watch video presentation and get a FREE performance management kit at
http://www.lifecycle-performance-pros.com
This presentation takes you through the steps of understanding your business intelligence needs and identifying the right tools for you. We discuss the different types of BI tools. We to discuss the criteria for selecting each type of tools. We to discuss popular Business Intelligence vendors and how to rate them. And we are going to discuss the job functions and responsibilities for a typical BI implementation
Self Service Reporting & Analytics For an EnterpriseSreejith Madhavan
- Enterprise organizations have legacy solutions as well as emerging solutions
- Optimizing the solution for right audience and right use-cases is critical for adoption across user-base
Understanding Identity Management with Office 365Perficient, Inc.
As more companies leverage Office 365, identity management between on-premise and cloud has become a topic of increasing importance. Fortunately, Office 365 offers a wide range of different identity management options that you can select based on your organization’s needs and preferences.
Join Perficient as we take a look at:
What constitutes identity management in Office 365
Federation and synchronization options available with Office 365, including ADFS and DirSync with password synchronization
Multi-forest deployments and deploying infrastructure using Windows Azure
B6 - An initiative to healthcare analytics with Office 365 & PowerBI - Thuan ...SPS Paris
Today data is a valuable asset in every organization, especially in healthcare industry. For example, with data about number of patients by location, hospital shall have the ability to offer more services to take care of them rapidly by building more medical stataion. Or with doctor's workload you know how to start hiring more human resources to balance the workload. With Office 365 - a digital workplace platform and PowerBI - a business intelligence and analytics on Microsoft Cloud service, let's have a look at how the digital transformation is initiated for healthcare industry.
Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes.
The Business Benefits of a Data-Driven, Self-Service BI OrganizationLooker
Watch the webinar at http://bit.ly/1LzzuIo
Self-service Business intelligence software is bringing analysts and business users together and driving the fundamental cultural shift making organizations truly data-driven. Broader access to reliable and curated data can improve business performance with top- and bottom-line impact. And more businesses are seeing this benefit as interest in self-serve BI tools grows, according to TDWI research.
AWS Summit Singapore - Accelerate Digital Transformation through AI-powered C...Amazon Web Services
Andrew McIntyre, Director of Strategic ISV Alliances, Informatica
Modernizing your analytics capabilities to deliver rapid new insights is critical to successfully drive data-driven digital transformation. Many organizations find it challenging to connect, understand and deliver the right data to generate new insights. Learn about the latest patterns, solutions and benefits of Informatica's next-generation Enterprise Data Management platform to unleash the power of your data through the modern cloud data infrastructure of AWS. See how you can accelerate AI-driven next-generation analytics by cataloging and integrating structured and unstructured data from hundreds of data sources from multiple on-premises and cloud data sources.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
AI-SDV 2020: Bringing AI to SME projects: Addressing customer needs with a fl...Dr. Haxel Consult
Customers interested in Language Analytics solutions typically approach us with a broad range of business cases and specific business needs. Especially when it comes to the data available for their case and for any AI aspects involved, the variation in data types, data quality and data quantity is, by our experience, quite vast and at the same time so critical for a project's success, that we often start our requirements analysis right there: at the data. At Karakun, our Language Analytics team addresses this in an increasingly flexible way: We select from a set of Language Analytics tools and related services (e.g. data cleansing and data procurement) to meet the business needs at hand with the data available or at least in reach – at reasonable costs.
The methodology stack ranges from heuristic logic over statistical solutions to neural networks. At the same time, we aim at reducing the amount of data needed for such training, e.g. by integrating state-of-the-art neural technologies into our platform. That way, also SMEs and their specific business cases can benefit from the full range of Language Analytics options.
To illustrate our approach, we will present an e-Safe solution which allows for semantic document tagging and search in highly secured virtual safes. In addition, our solution provides text-based triggers for complex workflows depending on the safe´s content.
This presentation has been uploaded by Public Relations Cell, IIM Rohtak to help the B-school aspirants crack their interview by gaining basic knowledge on IT.
F.A.I.R Data Principles with Knowledge Graphs & AI. Challenges and opportunities with emerging new technologies and paradigm shift of information management and data governance.
Executing successfully a Knowledge Graph initiative in an organization requires a series of strategic decisions that need to be taken before and during the execution.
Issues like how to balance the (inevitable) knowledge quality trade-offs, how to prioritize knowledge evolution, or how to allocate resources between new knowledge delivery and technology improvement, are often not contemplated early or adequately enough, resulting into frictions and sub-optimal results.
In this talk, I describe some key strategic dilemmas that Architects and Executives face when designing and executing Knowledge Graph projects, and discuss potential ways to deal with them.
Microsoft is continually adding new features to Office 365, and it is sometimes easy to get lost in information. This is particularly true when you need to deploy new functionality in your own organization.
This session explores records management in Office 365 and SharePoint. What is useful, what could be improved, and what are the potential drawbacks? Understand the importance of metadata – in driving records, the synergy with classification labels in the Office 365 Security and Compliance Center, and how it is part of effective records management.
Still worried about classification errors made by your end users? See how we solved that problem years ago.
Speakers:
Michael Paye – Chief Technology Officer at Concept Searching
Robert Piddocke – Vice President of Channel and Business Development
In this engaging, 1-hour webinar (hosted by http://www.poolparty.biz and http://www.mekon.com), you will learn how to tailor information chunks to readers’ unique needs. We will talk about:
- Benefits and principles of granular structured content, and how to start preparing your own content for this new architecture.
- Best practices for linking structured content to standards-based taxonomies, and some pitfalls to avoid
- The underlying semantic architecture that you can work toward for a truly mature and scalable approach to linking content and data
- Key use cases that you can apply to your own organization
Ariadne: First Report on Natural Language Processingariadnenetwork
D16.2 - Exploration of use of Natural Language Processing (NLP) to aid resource discovery which focuses on "grey literature". Both rule-based and machine learning are considered and one application also covers metadata extraction and enrichment.
Self Service Reporting & Analytics For an EnterpriseSreejith Madhavan
- Enterprise organizations have legacy solutions as well as emerging solutions
- Optimizing the solution for right audience and right use-cases is critical for adoption across user-base
Understanding Identity Management with Office 365Perficient, Inc.
As more companies leverage Office 365, identity management between on-premise and cloud has become a topic of increasing importance. Fortunately, Office 365 offers a wide range of different identity management options that you can select based on your organization’s needs and preferences.
Join Perficient as we take a look at:
What constitutes identity management in Office 365
Federation and synchronization options available with Office 365, including ADFS and DirSync with password synchronization
Multi-forest deployments and deploying infrastructure using Windows Azure
B6 - An initiative to healthcare analytics with Office 365 & PowerBI - Thuan ...SPS Paris
Today data is a valuable asset in every organization, especially in healthcare industry. For example, with data about number of patients by location, hospital shall have the ability to offer more services to take care of them rapidly by building more medical stataion. Or with doctor's workload you know how to start hiring more human resources to balance the workload. With Office 365 - a digital workplace platform and PowerBI - a business intelligence and analytics on Microsoft Cloud service, let's have a look at how the digital transformation is initiated for healthcare industry.
Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes.
The Business Benefits of a Data-Driven, Self-Service BI OrganizationLooker
Watch the webinar at http://bit.ly/1LzzuIo
Self-service Business intelligence software is bringing analysts and business users together and driving the fundamental cultural shift making organizations truly data-driven. Broader access to reliable and curated data can improve business performance with top- and bottom-line impact. And more businesses are seeing this benefit as interest in self-serve BI tools grows, according to TDWI research.
AWS Summit Singapore - Accelerate Digital Transformation through AI-powered C...Amazon Web Services
Andrew McIntyre, Director of Strategic ISV Alliances, Informatica
Modernizing your analytics capabilities to deliver rapid new insights is critical to successfully drive data-driven digital transformation. Many organizations find it challenging to connect, understand and deliver the right data to generate new insights. Learn about the latest patterns, solutions and benefits of Informatica's next-generation Enterprise Data Management platform to unleash the power of your data through the modern cloud data infrastructure of AWS. See how you can accelerate AI-driven next-generation analytics by cataloging and integrating structured and unstructured data from hundreds of data sources from multiple on-premises and cloud data sources.
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
AI-SDV 2020: Bringing AI to SME projects: Addressing customer needs with a fl...Dr. Haxel Consult
Customers interested in Language Analytics solutions typically approach us with a broad range of business cases and specific business needs. Especially when it comes to the data available for their case and for any AI aspects involved, the variation in data types, data quality and data quantity is, by our experience, quite vast and at the same time so critical for a project's success, that we often start our requirements analysis right there: at the data. At Karakun, our Language Analytics team addresses this in an increasingly flexible way: We select from a set of Language Analytics tools and related services (e.g. data cleansing and data procurement) to meet the business needs at hand with the data available or at least in reach – at reasonable costs.
The methodology stack ranges from heuristic logic over statistical solutions to neural networks. At the same time, we aim at reducing the amount of data needed for such training, e.g. by integrating state-of-the-art neural technologies into our platform. That way, also SMEs and their specific business cases can benefit from the full range of Language Analytics options.
To illustrate our approach, we will present an e-Safe solution which allows for semantic document tagging and search in highly secured virtual safes. In addition, our solution provides text-based triggers for complex workflows depending on the safe´s content.
This presentation has been uploaded by Public Relations Cell, IIM Rohtak to help the B-school aspirants crack their interview by gaining basic knowledge on IT.
F.A.I.R Data Principles with Knowledge Graphs & AI. Challenges and opportunities with emerging new technologies and paradigm shift of information management and data governance.
Executing successfully a Knowledge Graph initiative in an organization requires a series of strategic decisions that need to be taken before and during the execution.
Issues like how to balance the (inevitable) knowledge quality trade-offs, how to prioritize knowledge evolution, or how to allocate resources between new knowledge delivery and technology improvement, are often not contemplated early or adequately enough, resulting into frictions and sub-optimal results.
In this talk, I describe some key strategic dilemmas that Architects and Executives face when designing and executing Knowledge Graph projects, and discuss potential ways to deal with them.
Microsoft is continually adding new features to Office 365, and it is sometimes easy to get lost in information. This is particularly true when you need to deploy new functionality in your own organization.
This session explores records management in Office 365 and SharePoint. What is useful, what could be improved, and what are the potential drawbacks? Understand the importance of metadata – in driving records, the synergy with classification labels in the Office 365 Security and Compliance Center, and how it is part of effective records management.
Still worried about classification errors made by your end users? See how we solved that problem years ago.
Speakers:
Michael Paye – Chief Technology Officer at Concept Searching
Robert Piddocke – Vice President of Channel and Business Development
In this engaging, 1-hour webinar (hosted by http://www.poolparty.biz and http://www.mekon.com), you will learn how to tailor information chunks to readers’ unique needs. We will talk about:
- Benefits and principles of granular structured content, and how to start preparing your own content for this new architecture.
- Best practices for linking structured content to standards-based taxonomies, and some pitfalls to avoid
- The underlying semantic architecture that you can work toward for a truly mature and scalable approach to linking content and data
- Key use cases that you can apply to your own organization
Ariadne: First Report on Natural Language Processingariadnenetwork
D16.2 - Exploration of use of Natural Language Processing (NLP) to aid resource discovery which focuses on "grey literature". Both rule-based and machine learning are considered and one application also covers metadata extraction and enrichment.
HappyDev-lite-2016-весна 01 Денис Нелюбин. Вкалывать на роботовHappyDev-lite
Всё меняется. Всё меняется настолько быстро, что скоро мы перестанем успевать за изменениями. Роботы. Промышленные уже здесь. Бытовые появляются. Уже есть роботы-шахматисты и роботы-врачи. Скоро будут роботы-шоферы и роботы-слуги. Что дальше? Чем будут заниматься человеки?
Время поспекулировать, пофилософствовать и похоливарить. Пока есть время.
See ways to improve OCR accuracy on document scans. Cleaning and enhancing images can greatly improve the accuracy of OCR interpretations on your documents. Learn about automatic sophisticated adaptive thresholding, text smoothing and more. Add field validation and preview and testing features for optimal OCR interpretation.
Performance of Statistics Based Line Segmentation System for Unconstrained H...AM Publications
Handwritten character recognition is a technique by which a computer system could recognize characters and other symbols written in natural handwriting. Segmentation decomposes the document image into subcomponents like lines, words and characters. To achieve greater accuracy, segmentation and recognition could not be treated independently. Most of the existing line segmentation methods have limitations when applied to unconstrained handwritten documents. Statistics based line segmentation system was developed in Java Developer Kit 1.6 for segmenting unconstrained handwritten document images into lines. Arithmetic mean, trimmed mean and inter-quartile mean were used appropriately to achieve accurate segmentation results. The performance of the system was studied by using a few public handwritten document image datasets and images collected from different writers to compare its segmentation accuracy. The datasets contained well separated, sharing, touching, overlapping, irregular base and short handwritten text lines. The samples from the datasets were also segmented by a few other line segmentation methods. The segmentation accuracy of the system was higher than that of other methods. Performance measures like language support, segmentation document and line type of the system were compared with that of other line segmentation methods. The developed system segmented handwritten and printed lines from English, Chinese and Bengali languages and supported linear and non linear lines.
Best Practices for Large Scale Text Mining ProcessingOntotext
Q&A:
NOW facilitates semantic search by having annotations attached to search strings. How compolex does that get, e.g. with wildcards between annotated strings?
NOW’s searchbox is quite basic at the moment, but still supports a few scenarios.
1. Pure concept/faceted search - search for all documents containing a concept or where a set of concepts are co-occurring. Ranking is based on frequence of occurrence.
2. Concept/faceted + Full Text search - search for both concepts and particular textual term of phrase.
3. Full text search
With search, pretty much anything can be done to customise it. For the NOW showcase we’ve kept it fairly simple, as usually every client has a slightly different case and wants to tune search in a slightly different direction.
The search in NOW is faceted which means that you search with concepts (facets) and you retrieve all documents which contain mentions of the searched concept. If you search by more than one facet the engine retrieves documents which contain mentions of both concepts but there is no restriction that they occur next to each other.
Is the tagging service expandable (say with custom ontologies)? also is it a something you offer as a service? it is unclear to me from the website.
The TAG service is used for demonstration purposes only. The models behind it are trained for annotating news articles. The pipeline is customizable for every concrete scenario, different domains and entities of interest. You can access several of our pipelines as a service through the S4 platform or you can have them hosted as an on premise solution. In some cases our clients want domain adaptation or improvements in particular area, or to tag with their internal dataset - in this case we offer again an on premise deployment and also a managed service hosted on our hardware.
Hdoes your system accomodate cluster analysis using unsupervised keyword/phrase annotation for knowledge discovery?
As much as the patterns of user behaviour are also considered knowledge discovery we employ these for suggesting related reads. Apart from these we have experience tailoring custom clustering pipelines which also rely on features like keyword and named entities.
For topic extraction how many topics can we extract? from twitter corpus wgat csn we infer?
For topic extraction we have determined that we obtain best results when suggesting 3 categories. These are taken from IPTC but only the uppermost levels which are less than 20.
The twitter corpus example is from a project Ontotext participates in called Pheme. The goal of the project is to detect rumours and to check their veracity, thus help journalists in their hunt for attractive news.
Do you provide Processing Resources and JAPE rules for GATE framework and that can be used with GATE embedded?
We are contributing to the GATE framework and everything which has been wrapped up as PRs has been included the corresponding GATE distributions.
At IDenTV our mission is to create powerful video analysis capabilities and actionable insights from Video Big Data. Transparency for ad-buyers & advanced analytics to sellers bridging the gap between TV & digital/social video providing better measurement, decision support & eventually the goal of facilitating programmatic multi-platform video-marketplaces. For years IDenTV has been the only software based technology company that does not rely on intrusive or manual processes. Further, we are the only company to push the bounds of deploying and executing this type of advanced analysis in REAL-TIME while remaining highly efficient - both in cost and resources.
IDenTV’s IVP™ & “Video Juicer™”: Real-time automated content recognition & artificial intelligence powered video analytics platform, which accurately produces rich contextual metadata from large amounts of video.
Identifying: Faces, Objects, Brand/logos, Activities, Scenes, CC extraction, multi-lingual ASR, Geo Location, NLP/Semantics & more. Integrating with any type or video source (Live TV, VOD, Archive), with modules designed to be plug-and-play for better performance. Create numerous value propositions to the media & entertainment industry including:
- Ad verification parsed by location and user metadata in real-time
- Ad-Ops/Marketing Workflow Automations: Real-Time Video Verified Post-Log (“as run”) Generation
- Brand Safety
- Rights management & Copyright and piracy alerts
- Content Moderation: Determine content (both good and bad) upon initial upload based on predefined criteria. Pinpointing illicit content or activity, actions or threats geo-spatially (jihadi, bomb-making, torture, etc.)
- Advanced Ad targeting through connective analysis of context in posted images and videos (hyper-targeting based on content and behavior of user – “Contextual Hyper-Targeting”)
- Synchronized Cross-channel Marketing: Event triggers from streaming or live TV push targeted ads to re-targeted viewers on smart/mobile devices, increasing ROI to advertisers by connecting brands with high-intent high-value users.
For years IDenTV has been the only software-based technology company that does not rely on intrusive or manual processes for advanced real-time artificial intelligence powered automated content recognition. Producing unparalleled insights and valuable analytics from vast repositories of video. Via innovation IDenTV is augmenting and optimizing how video analysis is done and how analytics are produced and consumed. IDenTV strives to continuously drive value across the media industry and beyond with our innovative technology stack and talented team of engineers and scientists.
Neural Networks in the Wild: Handwriting RecognitionJohn Liu
Demonstration of linear and neural network classification methods for the problem of offline handwriting recognition using the NIST SD19 Dataset. Tutorial on building neural networks in Pylearn2 without YAML. iPython notebook located at nbviewer.ipython.org/github/guard0g/HandwritingRecognition/tree/master/Handwriting%20Recognition%20Workbook.ipynb
You've heard about TotalAgility 7.0, the world's first unified platform for the development and deployment of smart process applications. But did you know it is available both on-premise and in the Cloud? In this presentation you will understand when it makes sense to deploy TotalAgility as a service, and the benefits this type of deployment delivers. You will also learn about the Cloud-specific features and licensing available in the newly announced TotalAgility 7.1 release.
IBM Watson Content Analytics: Discover Hidden Value in Your Unstructured DataPerficient, Inc.
Healthcare organizations create a massive amount of digital data. Some is stored in structured fields within electronic medical records (EMR), claims or financial systems and is readily accessible with traditional analytics. Other information, such as physician notes, patient surveys, call center recordings and diagnosis reports is often saved in a free-form text format and is rarely used for analytics. In fact, experts suggest that up to 80% of enterprise data exists in this unstructured format, which means a majority of critical data isn’t being considered or analyzed!
Our webinar demonstrated how to extract insights from unstructured data to increase the accuracy of healthcare decisions with IBM Watson Content Analytics. Leveraging years of experience from hundreds of physicians, IBM has developed tools and healthcare accelerators that allow you to quickly gain insights from this “new” data source and correlate it with the structured data to provide a more complete picture.
[Webinar Slides] How to Increase Your Profits by Improving Your Data AccuracyAIIM International
If you're extracting data, how do you know if your methods are yielding the best results? Are you extracting the right information?
How do you really know if the data accuracy upon which you depend is as good as you claim? Follow along with these webinar slides to understand how to perform your own accuracy audit to answer these questions and learn how to improve your data extraction.
Want to follow along with the webinar replay? Download it here for free: http://info.aiim.org/improving-your-data-accuracy
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The Blended Learning And Technologies Forum (BLAT Forum) is a quarterly event in Auckland, New Zealand that welcomes practitioners, designers and developers of blended learning instructional deliverables across different industries of the New Zealand economy.
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In this presentation you can learn about:
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this presentation is prepared by me and by friend @alina dangol. This is basically the slide related to the design of a system, how to generate forms & reports, about normal forms as well as file organization
Sara Nash and Urmi Majumder, Principal Consultants at Enterprise Knowledge, presented on April 19, 2023 at KM World in Washington D.C. on the topic of Scaling Knowledge Graph Architectures with AI.
In this presentation, Sara and Urmi defined a Knowledge Graph architecture and reviewed how AI can support the creation and growth of Knowledge Graphs. Drawing from their experience in designing enterprise Knowledge Graphs based on knowledge embedded in unstructured content, Sara and Urmi defined approaches for entity and relationship extraction depending on Enterprise AI maturity and highlighted other key considerations to incorporate AI capabilities into the development of a Knowledge Graph.
View presentation below in order to learn about how:
Assess entity and relationship extraction readiness according to EK’s Extraction Maturity Spectrum and Relationship Extraction Maturity Spectrum.
Utilize knowledge extraction from content to gather important insights into organizational data.
Extract knowledge with three approaches:
RedEx Rule, Auto-Classification Rule, Custom ML Model
Examine key factors such as how to leverage SMEs, iterate AI processes, define use cases, and invest in establishing robust AI models.
Content services to capture and scale your expertise
SharePoint Syntex uses advanced AI and machine teaching to amplify human expertise, automate content processing, and transform content into knowledge.
Content understanding
Create AI models that capture expertise to classify and extract information and automatically apply metadata.
Capture expertise with AI
Build no-code AI models that teach the cloud to read content the way you do.
Enrich content and metadata
Find key facts in your content to improve search and teamwork.
Content processing
Automate the capture, ingestion, and categorization of content and streamline content-centric processes.
Automatically classify content
Use advanced AI in SharePoint Syntex to capture and tag structured and unstructured content.
Streamline content processes
Integrate with Power Automate to build workflows that leverage extracted metadata.
Content compliance
Connect and manage content to improve security and compliance.
Integrate content across systems
Connect SharePoint Syntex to content inside and outside Microsoft 365.
Protect and manage content
Enforce security and compliance policies with automatically applied sensitivity and retention labels.
Learn how to create a scalable document workflow to consistently produce error-free documents
Make it easy to create, manage, collaborate on, and store case files and court forms.
Though managing forms and documents is a critical part of any law practice, the task itself can be tedious and time-consuming—more so if your firm’s document workflow is not user-friendly.
Cloud-based legal document automation helps law firms easily produce, securely store, and efficiently manage documents…saving your staff the time spent manually reviewing and organizing every single file.
Join this free CLE-eligible webinar to find out how to leverage document and court form cloud solutions to bring more efficiency to your practice.
In this CLE-eligible webinar, you’ll learn:
How cloud-based document tools improve production, storage, accessibility and submission of legal documents and court forms
Best practices for creating flexible document workflows and templates (including tips for formatting and styling MS Word documents)
How to use document automation solutions to keep information secure, reduce errors and malpractice risk.
https://www.clio.com/events/webinar-manage-docs-and-forms/
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Similar to Introducing Compreno - Natural Language Processing Technology (20)
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. ABBYY Worldwide
2
Global
16 offices with more than 1.250 employees
in Europe, USA, Asia, Australia und Russia
Innovative
27% revenue investment in R&D,
more than 400 developers and scientists
Reliable
Connected
Trusted partner to over 1000 companies in
more than 150 countries around the world
Successful
> 40 million software users process more than
9 billion pages per year with ABBYY products
Enabling
Recognise, capture, (translate), analyse –
we transform information into action
Strong and independent core technology that
evolves with the needs of the digital revolution
3. Digital Universe
2.5 Exabyte of data generated every day = 2.5 Mio Terabyte = 2.5 x 1018 Byte
(source: Northwestern University, 2016)
Majority (ca. 80%) is unstructured
3
1.4 x 1014 Word pages
3.5 x 1013 PPT slides
2 x 1013 PDF pages (image & text)
2 x 1014 emails
4 x 1013 scanned pages
3 x 1013 images (.tiff)
1.4 x 1016 .txt files
(source for average file sizes: netdocuments.com, 2016)
Reports, brochures, datasheets, presentations,
research documents, service documents,
pricelists, process descriptions, project
descriptions, product feature specifications,
customer communication, accident/security
reports, contracts, email, web texts, articles in
magazines, complete intranets …
4. Unstructured Content I
What do unstructured documents have in common?
● They are composed in natural language
What is the problem about natural language?
● Complex to analyse and summarise
● Does have a structure but is not standardized (different people use different terms, expressions, syntax to talk
about the same thing)
● Content is unexpected and cannot be processed with rules
● Limited/no metadata
4
5. Unstructured Content II
● The computer does not know what the document is about and there is no source to
get this information from
● Information is “locked” within documents
● Information that may be valuable, or confidential, business-critical, or defensibly deletable, but is
difficult to find and manage
There is no business value in content that can’t be analysed or found
Natural language requires dedicated processing technology
5
6. ABBYY Compreno
6Confidential
What is it? Natural Language Processing (NLP) technology
What does it do? Advanced automated text analysis
● Gathers information about a document from the document
● Understands meaning of words within context
● Reveals relationships between words
● Builds stories across documents
● Extracts insights and intelligence from unstructured text
7. How Compreno works
Key Components
7
Semantics
Semantic analysis is used
to interpret syntactic structures
in terms of universal,
language-independent
concepts and their relations.
Syntax
Identifies formal relations among
words in a sentence or across
several sentences.
The system analyzes a text
and builds a tree of syntactic
relations.
Statistics
Data gleaned from parallel and monolingual corpora are
used for training the analysis algorithms and verifying and
expanding the formal descriptions available to the system.
Semantics
Syntax
Statistics
8. ABBYY Compreno
Platform for document understanding
Core uses of Compreno technology
● Classify unstructured documents
● Identify and extract entities, facts and
events from texts
8Confidential
10. 10
Mammals Birds Reptiles Fish
What is classification?
… to
Categorisation based on particular shared features
11. How document classification works
Three main steps
11
Training
Set up model, define categories,
select/collect training documents, train
model, choose best algorithm
Test and tune
Analyse test results, eliminate
mistakes, adjust training set,
retrain model
Classification
Deploy model to
production, classify
documents
12. Document classification – Why?
12
Essential step in information management
Enable advanced analysis and decision-
making
Generate business value
13. Why is classification not as easy as it seems?
Building up a reliable classification workflow is difficult…
13
Big Content
Technical challenges
- Big training sets
- Complex algorithms
- Difficult to integrate
Business challenges
- Traditional classification
methods don’t do the job
- High investments for
building and maintaining
the rule sets and
classification schemes
required (classification
expert knowledge)
New,
dedicated
processing
methods
required
Unstructured documents
14. ABBYY Smart Classifier
● Text classification module for organising unstructured documents
● Assign unseen documents to predefined categories based on statistical,
morphological and semantic analysis
● Uses supervised machine learning to produce a classification model from sample
inputs
● Classification creates meta data derived from the document context
14
Next generation document classification
15. Unstructured information processing
● Unlock information
● Make content searchable, accessible and retrievable
Automated classification
● High speed
● Constant quality
● No manual work
Semantic-based classification
● Deep text analysis techniques employed for even more accurate classification
15
Smart Classifier features and values
16. Smart Classifier features and values
Machine learning
● System learns automatically based on the training documents
● No particular knowledge required to setup classification
● No specification of rules necessary
● Small training sets
Automatic algorithm optimisation
● Selection of the best-performing algorithm for each document set
16
17. Smart Classifier features and values
Simple UI
● No specific knowledge required to create a model, train the system and launch a
classification workflow
Input document formats and languages
● Process content regardless of original format
● OCR for processing of images
● 39 classification languages
17
18. IT Integration of Smart Classifier
Leverage existing systems and infrastructure
18
19. Smart Classifier Workflows
19
Create and deploy classification model
01 | Category definition and selection of sample documents
02 | Setup of classification model
03 | Model training
04 | Model testing, quality evaluation and tuning
05 | Deployment to production
Document classification workflow
20. 01| Category definition and selection of sample documents
● Category = a group of documents that have particular shared features
● Category definition is a management decision, no special IT skills required
● Content and process experts select representative documents for each category
● Minimum: 10 documents per category
● For reliable statistics: ±100 documents per category
● Representative sample of documents
● Documents must be typical for category: The more representative of the respective
category a document is, the better the model will perform (garbage in, garbage out).
● Proportion of docs assigned to each category should be the same as in the collection of
documents to be classified
● Smart Classifier accepts many formats (plain text, Office, HTML, XML, PDFs
(Image formats are submitted to OCR))
● Folder structure: Each (sub-)category = dedicated (sub-) folder
● Create training set and control set and save them as ZIP files
20
21. 02| Setup of classification model
● The Classification Model defines, how and by which categories document
classification will be performed.
● Model creation via Model Editor web UI or REST API (code samples included in
documentation)
● Set parameters
● Document language (39 languages supported)
● Category assignment (what category will be assigned to the document if more than one was
returned as candidate category)
● Quality criteria (trade-off between precision and recall)
21
22. 02| Setup of classification model
Model Editor web interface
22
23. 03| Model training
● Load training documents
● Train classification model
● Machine learning
● The system automatically
identifies and uses the most
relevant features from the
training documents for
creating the classification
model
23
24. 04| Model testing, quality evaluation and tuning
● Load and test control set to determine whether training process was successful
● Classification results in control set must meet expectations before model can be deployed
● Model Editor provides instant visibility of each document within a classification
project
● Source text and key words picked by the algorithms can be analysed and checked
● Terms that should be ignored during classification can be added to a stop word list
● Analyse: F-measure, precision, recall
● Debug: Confidence level, selected keywords
● Adjust: Inclusiveness, stop words, documents in classes (re-assign category)
● Upload further training/control documents
24
26. 05| Deployment to production
● When the model is deployed it
becomes available via the
Compreno REST API
● If you make changes to the model,
it needs to be retrained for changes
to become effective
26Confidential
27. Document classification workflow
27Confidential
Once the system is set up and a classification model is published for operation, incoming
classification tasks will be accepted
01| A new document classification task is created
02| The document is converted into an internal format
03| The document is classified
04| The document classification results are saved
05| The task is completed
29. Smart Classifier application scenarios
Enterprise content management and its subdomains
Archiving, records management (Information Governance), document management,
enterprise search
● Classification of incoming and stored documents
● Definition of category-based access rights and retention policies
● Search enhancement
29
30. Smart Classifier application scenarios
Information lifecycle
Manage
Store
Archive
Dispose
Create
Capture
30
Classification of incoming
documents
Add documents to the
system that have a value, i.e.
are enhanced with metadata
Classification for aid in risk mitigation
Category-based document access
rights
Category-based disposal
policy
Classification for aid in compliance
Category-based retention policy
Classification to improve enterprise
search systems
Add class to search index
Category-based routing and distribution
Post-process
• Classification for metadata correction
• Classification of legacy content for data
improvement
31. Smart Classifier application scenarios
Data migration
● Organise content before, during or after migration
Client support
● Category-based prioritisation and routing of client issues shorten response times
eDiscovery
● Quickly gather and prepare documents
Mailroom
● Automatically select the most suitable processing workflow
E-mail management
● Additional metadata facilitates and accelerates routing
31
32. Smart Classifier benefits
For all enterprises
Create access to
information
Efficient
information
management
Aid compliance &
risk mitigation
Cost efficiency
32
35. ABBYY Compreno
Platform for document understanding
Core uses of Compreno technology
● Classify unstructured documents
● Identify and extract entities, facts and
events from texts
35Confidential
36. ABBYY InfoExtractor SDK
● Information extraction module for processing natural language texts
● Natively processes unstructured documents and accesses the embedded textual
information
● Identifies different facts, entities and the relationship between them
● Automatically extracts critical data
● Combines related data into facts
36Confidential
37. How InfoExtractor works I
From text to semantics
Syntactic parsing: Determine the structure of the input text; understand how concepts relate to one another
within one or more sentences
Semantic parsing: Contextual analysis = Obtaining and representing the meaning of a
sentence
Universal Semantic Hierarchy: Language
independent hierarchy of concepts to reflect the
meaning and relations of words and sentences
Derive meaning of sentence by
understanding of the context and the
“speaker's” intent.
An ontology is a formal representation of
concepts and the relationships between
those concepts.
Lexical analysis: Convert sequence of characters into sequence of words
Morphological analysis: Analyse the structure of words and parts of words
38. Connect entities with other entities and facts, even if the words that define them are replaced with
pronouns or omitted in the text
Example: The company has denied reports it is preparing to default on its loans if it cannot reach
agreement on its bailout terms with international creditors
38
How InfoExtractor works II
Identify relationships between words
Get the
complete story
40. Example: Some people work with PDF documents but not all employees do.
40
Don’t miss any
valuable facts
How InfoExtractor works IV
Detect omitted words
41. InfoExtractor features and values
41Confidential
Natural Language Processing
● Understand the meaning of words and relations between them
Extraction of entities and events
● Extract the facts and story lines embedded in unstructured information
● Persons, organisations, dates
● Deals, purchases, employment details
Identify relationships between entities and events
● Contracting parties, subject of the contract, financial figures
42. InfoExtractor features and values
Basic and custom ontologies
● Basic ontologies including widely used words
● Custom ontologies for industry solutions
Customized entities for specific cases
● Custom ontology dictionaries to extract complicated examples of entities (e.g. Asian
names or companies)
Input document formats and languages
● Work with text regardless of source
● English, Russian, German
● OCR embedded for image processing
42Confidential
45. InfoExtractor application scenarios
Contract Management
● Use Case: Mass contract ingestion
● Document Type: Contract
● Customer: ISVs, Service Providers
● Benefit: Extend service offering & increase revenues
Customer On-Boarding
● Use Case: Capture & upload customer information at point of entry into the system
● Document Type: Statuary documents, contracts
● Customer: Banks, insurance companies
● Benefit: Accelerate document processing
45
46. InfoExtractor application scenarios
Applicant Tracking
● Use Case: Tag and upload CVs to improve search
● Document Type: CV
● Customer: HR departments
● Benefit: Minimise resources required to process all the necessary CVs
Credit Risk Mitigation
● Use Case: Decide on providing loans; check various sources of information on potential loan customers.
● Document Type: Contracts, statuary documents, court decisions
● Customer: Banks
● Benefit: Accelerate document processing
46
47. InfoExtractor benefits
Get decision-critical information with less costs and efforts
Intelligence and
insights
Aid predictive
decision making
Uncover hidden
risks
Cost efficiency
47
Use analytics to create
new value out of existing
and new data
Get the big picture by
connecting entities, facts
and events across
documents
Accelerate and automate
content upload and
analysis to optimise
manual processes
Take critical decisions
faster based on relevant
information
48. 48
Good classification and information
extraction let organisations solve
tasks they are not capable of solving
at the moment
Smart Classifier and InfoExtractor
make document classification and
information extraction simple
Summary
49. Licensing
● Smart Classifier and InfoExtractor are available for testing via time and volume limited
trial license
● Different license models
● Perpetual with software maintenance
● Subscription (yearly)
● OEM licensing
● Standard license model based on renewable peak volume
● Backend can be scaled up
49
ABBYY is a leading provider of text recognition and document conversion technologies and services.
Operating globally, ABBYY is headquartered in Moscow, Russia, with offices in Germany, the UK, the United States, Canada, Ukraine, Cyprus, Australia, Japan and Taiwan.
ABBYY offers a broad range of solutions designed for specific business and industry needs, ideally suited to meet their individual requirements while seamlessly integrating in internal workflows.
Organisations all over the world use ABBYY solutions to optimise their paper-intensive business processes.
Key components
ABBYY Compreno uses three major components — semantics (in the form of a language-independent hierarchy of concepts), syntax (i.e. the ability to understand how concepts relate to one another within one or more sentences) and statistical data, which is used for combining words into natural-sounding sequences and as an aid in sense disambiguation.
Language-independent hierarchy of concepts = Universal Semantic Hierarchy (USH)
Key to ABBYY’s Compreno technology is the idea that people speak in different languages but think using similar concepts. For example all people live in houses, have furniture, use phones, or drive cars. These concepts are common to all people and are language-independent. Therefore, we can build a semantic hierarchy of concepts that will work for all languages. The ABBYY Compreno semantic hierarchy is a tree-like structure, with the thick branches representing more general concepts (e.g. “furniture”) and the thin branches representing more specific concepts (e.g. “bed”, “cupboard”, “chair”). This tree-like structure contains information about the combinability of its items and allows them to inherit properties from their parents. This approach helps resolve ambiguities during translation and provides more relevant search results. For example, there are different branches for the verb “to possess” in the hierarchy, one describing the idea of owning material things, and the other the ability of ideas, emotions and the like to dominate somebody’s mind.
Syntax
The syntax component detects how concepts are related to one another within one or more sentences. The system analyzes texts and builds a tree of syntactic relations. To make syntactic parsing more accurate, ABBYY Compreno also relies on semantic analysis that makes use of the hierarchy of concepts described above. Joint use of the above components enables the system to “understand” sentences and either extract knowledge from them or express this understanding in another language.
Statistics
The third major component is statistics. ABBYY Compreno uses statistical data to generate naturally sounding word combinations and to better resolve ambiguities, which is necessary for correct parsing. Statistics are also used to distinguish homonyms in cases when even the semantic component does not provide a reliable answer. The statistical component uses texts of different genres and registers to reduce the likelihood of error and misinterpretation.
ABBYY Compreno is a natural language processing (NLP) technology that enables you to extract insights and intelligence from unstructured text.
ABBYY Compreno technology “understands” the meaning of words, reveals the relationships between them within content and uses this understanding to provide comprehensive text analysis that accurately identify entities, facts, events and relationships between them to discover the stories within textual documents.
Why do we need content classification at all?
Classification is an essential step in almost any kind of information or content management process.
Content can be routed through a process or assigned to a specific workflow according to class,
Category tagged content enhances enterprise search systems and allows knowledge workers to navigate through and retrieve information from huge repositories of data
Categories can be used in archiving content
Classification enables enterprises to leverage content, it creates access to information. In the classification process, incoming or stored content is recognised, differentiated and categorised for the purpose of further processing. Classification provides the basis for advanced text analysis, information extraction and information-based decision making
Classification not only helps businesses manage the tidal wave of data but generates business value also.
If classification is such an important step in information management why do so few organisations actually practice it? Why is classification obviously not as easy as it seems?
We can best answer this question when looking at the challenges enterprises face when it comes down to content classification:
Big Content
Today, the volume, velocity and variety of content generation are constantly increasing. Enterprises have to deal with huge data volumes, that they need to process and store. The more data there are, the harder it gets to search and locate critical data.
Unstructured format
The vast majority of information today is unstructured and composed in natural language. The problem about this type of content is that it is difficult to analyze and summarize because information is not standardized but unexpected and cannot be processed with extraction rules. As there is no or only limited metadata, the computer does not know what a document is about. The information is literally locked within the format and therefore unsearchable - information that may be valuable, or confidential, business-critical, or defensibly deletable, but is difficult to find and manage. In consequence, there is no business value in content that can’t be analyzed or found.
These challenges come along with a variety of technical challenges
Training of a classification systems requires many documents
Classification algorithms are hard to understand, parameters tuning is complex (if you do not know what how certain algorithms behave, how to know if you can trust and depend on the results)
Integration with existing enterprise systems and platforms is complicated or not possible at all (scientific classification libraries often work with plain text, no support for office formats, PDFs or images)
This on the other hand entails business challenges:
Traditional classification (manual, rule based) cannot meet these requirements any more.
Manual classification is expensive, slow, inconsistent (accuracy differs between individuals), quality deteriorates with increasing volumes and time pressure.
Rule based systems are basically unworkable for Big Content
High investments are required because classification is a complex domain and typically requires a skilled expert for setting up the classification workflow and developing, training and tuning the classification algorithm(s).
All this causes most classification projects to go unfinished.
To successfully manage these challenges and build up a reliable classification workflow new, dedicated processing technologies are required.
How does Smart Classifier solve the problem?...
Smart Classifier is a new, high-quality text classification module that has been designed for processing unstructured documents.
Smart Classifier assigns unseen documents to predefined categories based on morphological, statistical and semantic analysis of extracted text.
Smart Classifier uses supervised machine learning to automatically identify and use the most relevant features from a set of training documents, i.e. sample inputs, to build the classification model.
Smart Classifier gathers information about the document from the document and adds this information to the document as meta data. The classification result is a probability score for a single or multiple categories.
Unstructured information processing
Smart Classifier enables enterprises to unlock information from unstructured documents, turn it into an asset and use it to their advantage. In the classification step, content is converted to a searchable format and tagged with contextual metadata.
Automated classification
Automated classification overcomes most problems associated with manual classification
High speed:
Quickly classify incoming documents
Classify huge backlogs/repositories
Constant quality:
Manual classification quality deteriorates significantly under tight timelines
Manual classification quality varies between people
No manual work
Knowledge workers can focus on problem solving
Semantic based classification
Smart Classifier combines linguistics and statistics with semantic analysis for even more accurate classification. This functionality is currently available for Russian and English (German to come).
Machine learning
Smart Classifier applies machine learning algorithms to automatically train on small sets of sample documents and select the most appropriate classification features, i.e. it determines which features within the sample documents characterise each category.
The setup, training and deployment of classification in Smart Classifier does not require any specific knowledge.
It is not necessary, as with traditional rule based systems, to specify rule sets or to manually train and tune models with huge quantities of training documents.
The documents used for model training do not need to be pre-processed in any way.
Automatic algorithm optimisation
During the machine learning phase, Smart Classifier automatically tests multiple algorithms and selects the best-performing model and classification parameters for each document set. This makes the time intensive process of manual model tuning obsolete.
Simple UI
The Model Editor web interface is accessible for any business user to easily and quickly create and tune classification models.
Via Model Editor you can
Create classification projects
Set up classification models
Load training documents
Train models
Evaluate classification performance/Quality check
Refine models
Code samples for the Model Editor UI are included in documentation
The admin console provides an interface for IT staff for administration of Smart Classifier.
Variety of input document languages and formats
Smart Classifier natively processes a large variety of document formats including plain text, Microsoft® Office formats, HTML, PDFs, images, XML, and more. Image formats are pre-processed with OCR to extract text. Smart Classifier extracts the plain text from documents and uses it for classification. The extracted text can be saved for further processing or re-classification.
Smart Classifier offers automatic language detection and document classification for all major European and Asian languages.
Smart Classifier comprises multiple components for setup, training and administration of classification models and processing of classification tasks:
Processing Components:
Control Server/Service - System service that distributes tasks among the Processing Services.
Processing Station/Service - System service that processes documents in tasks assigned by the Control Service.
Admin Console - Administrative tool for managing ABBYY Smart Classifier (user accounts, licenses, tasks, event log,
Classification Model Server/Compreno Technology Module - Software component that contains classification algorithms and information extraction rules.
(Smart Classifier Data Service - System service that enables working with classification models)
Setup and training:
Model Editor – Web-based user interface for creating and managing classification projects and models.
Smart Classifier exists as a stand-alone entity, an external brain so to speak. It works as a service, is not domain specific and does not require a hard-coded classification workflow. Smart Classifier can process content from multiple sources like internal file share, email server, document repository, DMS, RMS.
Through its simple REST API Smart Classifier can easily be integrated into an existing IT environment.
Classification tasks and results are exchanged via the REST API:
Communication is carried out via HTTP calls that produce responses in JSON or RDF/XML format
Classification tasks can be submitted in synchronous, asynchronous or batch (.zip file) mode, depending on their amount and complexity.
The REST API can also be used for classification model setup, training and quality check (license parameter)
Smart Classifier provides two output formats for classification results, JSON or RDF/XML.
Results include information such as name of the classification model, categories with their probabilities, confidentiality flags, feature/word lists, access to the raw text (add-on license parameter) or error messages.
This information needs to be further processed in existing systems, workflows and solutions in order to derive value from it.
Scalable, server based architecture
Smart Classifier is based on a scalable backend, capable of processing large amounts of files. For a high throughput, it can be scaled both horizontally and vertically with additional processing resources. The maximum horizontal scalability is 20 processing services.
1. A new document classification task is created.
Tasks are created using the REST API. The Control Service chooses one of the available Processing Services and allocates the task to it. The task is then sent to the Processing Service.
2. The document is converted into an internal format.
The Processing Service converts the document into an internal format. If any text in the document requires optical character recognition (OCR), the station uses a built-in component to recognize the text. The availability of the OCR feature is determined by your current license.
3. The document is classified.
An executor requests the binary representation of the trained model from the Smart Classifier Data Service and classifies the document using the model.
4. The document classification results are saved.
The classification results are saved to an RDF/XML or a JSON file.
5. The task is completed.
The Control Service receives the RDF/XML or JSON file from the Processing Service and flags the task as completed. The task results may be obtained by means of the REST API
Smart Classifier can be deployed in a variety of scenarios across processes, workflows and projects.
Enterprise Content Management
The assumption is that probably every enterprise practices some sort of enterprise content management, be it using a file share, a simple workflow a fully fledge ECM solution or else. Enterprise content management is an umbrella term and encompasses, amongst others, archiving, records management (today called Information Governance), document management and enterprise search.
High-performance classification of unstructured content allows us to quickly organise large repositories and enables knowledge workers to efficiently search and locate information critical to their work.
In this context, Smart Classifier can be applied in the following tasks
Classify incoming documents to not simply add content to the system but add content that has a value, i.e. tagged with metadata
Once classified, incoming documents can be routed to their respective recipients based on category
Organise legacy content in projects identify and remove redundant, obsolete and trivial (ROT) content
Ensure compliance with regulatory and audit requirements by definition of
category-based document access rights to guarantee data security
category based retention policies, i.e. ensure that every important document is stored as long as it should be with accordance to the records management policies (defensible disposal)
Search enhancement: Generate additional metadata out of incoming and archived content and let knowledge professionals easily search and retrieve critical content via new facets
Besides enterprise content management there are other potential application scenarios for Smart Classifier
Data migration
Organise content before, during or after migration what to take and what to leave behind
Identify and remove duplicate and unnecessary content
Reduce volume of content to be migrated
Enterprises that go through events like M&As, corporate restructuring, system migration, system/storage consolidation, digitisation projects, and more that trigger need for content migration
Client support: Faced daily with tons of client issues, customer support employees need to classify, prioritise and route these. Automatic semantic-based classification can help to overcome this by shortening response times, improving customer satisfaction and retention
eDiscovery: Quickly gather and prepare documents for eDiscovery, audits and litigation
Mailroom. Automatically select the most suitable processing workflow, e.g. data extraction, straightly archiving, ….
E-mail management: Organising e-mails manually is painful, missing business critical messages from customers or suppliers is even more painful. Metadata (such as "to", "from") is rarely good enough. Using both metadata and content, new semantic-based classification automatically distinguishes the "wheat from the chaff".
We can derive the following benefits from Smart Classifier features and values….
Create access to information
Smart Classifier supports enterprises in accessing unstructured information, turning it into an asset and using it to their advantage.
Content and process experts can setup and maintain the classification, no special IT skills are required.
In unlocking information from the unstructured format, Smart Classifier makes content usable for downstream processes and routines. Classification provides the basis for advanced text analysis, information extraction and decision making.
Efficient information management
High-performance classification of unstructured content allows us to quickly organise large repositories and enables knowledge workers to efficiently search and locate information critical to their work
Automated classification with Smart Classifier greatly simplifies the entire classification process: It becomes easier, faster, more reliable and less costly. The quality of classification is always the same irrespective of workload.
Smart Classifier enables enterprises to quickly organize and prioritize unstructured content with category-based document routing, archiving, and filtering so that knowledge professionals can efficiently search and locate information critical for a variety of business tasks.
Automatic routing of incoming documents allows the acceleration and automatic selection of the most suitable category, workflow or responsible person.
Aid compliance & risk mitigation
Granular text- and semantic-based classification enables organisations to keep up with security, compliance and records management requirements. This is especially important given the impending EU GDPR regulation.
Automatic content classification enables you to identify data that should be discarded or archived at a targeted, granular level. Keep only the data that has a value and requires to be kept and get rid of data silos that only adds additional storage costs.
Minimize risk of data leakage or loss: Arrange your data leakage protection – make sure your confidential data is under control, does not flow outside and cannot be accessed by outsiders by applying content-aware classification-based access rights to documents.
Cost efficiency
With implementation of Smart Classifier enterprises increase the automation of organizational processes, while reducing processing costs. Less investments in manual work are required since most of the manual work associated with model training and tuning has been eliminated. Knowledge workers now can focus on problem solving. As a result, cost calculation becomes more reliable.
Identify and delete content that is redundant, obsolete or trivial (ROT) to reduce the space needed for storage
Smart Classifier can be easily integrated into information management routines to leverage existing infrastructure and investments
Create better customer applications
Extend the capabilities of existing product portfolio with easy to use classification
Enhance value proposition to your customers be innovative, offer new differentiator/USP
High usability: no special skills required on customer side to setup and maintain classification, content/process experts can do it
Quick ROI
Fast and cost-effective tool deployment with detailed documentation and code samples
Leverage and build upon existing investments in classification
Accelerate business processes
Enhance the efficiency of business processes to serve your customers better and faster
Easier cost calculation
Automated classification makes cost calculation easier because no manual work has to be planned and paid for. Automated classification is resistant to volume fluctuations at constant quality of classification results.
Save your customers costs by reducing staff resources
Classification is the first step to advanced text analysis and understanding. Once classified and tagged with contextual metadata, information is ready for further processing like search and retrieval, automated routing, intelligent data extraction and decision-making.
That brings us to the second ABBYY product powered by Compreno technology – InfoExtractor.
ABBYY InfoExtractor is a information extraction module that “understands” the meaning of words and identifies and extracts critical information from unstructured texts.
InfoExtractor takes up where Smart Classifier stops. It powers business tasks that require granular content analysis and understanding.
InfoExtractor provides comprehensive text analytics by automatically identifying and extracting business-relevant information from your content. It delivers insights and intelligence from unstructured information like contracts and reports
InfoExtractor applies deep linguistic analyses on the text in natural language to identify entities, persons, facts and relationships between them. However, not everything that is extracted from a sentence or document is wanted/needed. That’s why InfoExtractor “distills” the relevant information/facts/relationships.
InfoExtractor is an SDK: The extraction logic is very customer, project and domain specific. For different purposes different ontologies are necessary.
ABBYY's new approach
The ABBYY InfoExtractor (based on Compreno technology) analyses the text with different linguistic and statistical approaches. This results in massive meta-data that is created out of simple text. These “raw” linguistic hypotheses are then weighted, cross-checked with the embedded language and grammar rules. The best hypotheses are then matched against ABBYY's Universal Semantic Hierarchy to get the real (semantic) meaning and the context how the word is used in this sentence.
Natural Language Processing
Powered by Compreno technology, InfoExtractor understands the meaning of words and relations between them.
Extraction of entities and events
InfoExtractor accurately extracts information like entities, e.g. persons, organisations or dates, and facts, e.g. deals, purchases, employment, familiy relationships etc. from unstructured texts.
Identify relationships between entities and events
InfoExtractor identifies relationships between entites and facts like the subject of a contract (what is the contract about), who the involved parties are (related personal information) and what their roles (seller/buyer, employer/employee) are.
Analyse the deal that links a buyer and a seller and identify the related personal info, contacts or financial figures
Basic and custom ontologies
InfoExtractor SDK comes with basic ontologies that include widely used words
Industry ontologies for specific domains or tasks can be efficiently customized or created with the help of ABBYY professional linguistic services
Customized entities for specific cases
Custom ontology dictionaries can be used to handle particularly tough cases such as rare Asian names of people and companies.
New entities will automatically inherit existing extraction rules and require no additional descriptions.
Input document formats and languages
InfoExtractor natively processes a large variety of document formats including plain text, Microsoft® Office formats, HTML, PDFs, images, XML, and more. It extracts the plain text out of documents and uses it for analysis.
InfoExtractor can process texts in English, Russian and German.
Image formats are pre-processed with OCR to extract text.
InfoExtractor is a server-based module that works as a standalone entity within existing IT systems or can be integrated into solutions. It works as a service, is not domain specific and does not require a hard-coded workflow. InfoExtractor can process content from multiple sources like internal file share, email server, document repository, DMS, RMS.
InfoExtractor comprises multiple components for setup, training and administration of classification models and processing of classification tasks:
Control Server/Service - System service that distributes tasks among the Processing Services.
Processing Station/Service - System service that processes documents in tasks assigned by the Control Service.
Technology Module - Software component that contains classification algorithms and information extraction rules.
Admin Console - Administrative tool for managing ABBYY Smart Classifier (user accounts, licenses, tasks, event log)
Custom Data Server - A system service that enables working with semantic and ontology user dictionaries and optimizes the algorithm that calculates confidence scores for extracted data.
Through its simple REST API InfoExtractor can easily be integrated into an existing IT environment.
Info extraction tasks and results are exchanged via the REST API:
Communication is carried out via HTTP calls that produce responses in JSON or RDF/XML format
Tasks can be submitted in synchronous or asynchronous mode, depending on their amount and complexity.
InfoExtractor provides two output formats for results, JSON or RDF/XML.
The results contain information about entities, facts, and events, confidentiality flags, access to the raw text (add-on license parameter) or error messages.
This information needs to be further processed in existing systems, workflows and solutions in order to derive value from it.
Scalable, server based architecture
InfoExtractor is based on a scalable backend, capable of processing large amounts of files. For a high throughput, it can be scaled both horizontally and vertically with additional processing resources. The maximum horizontal scalability is 20 processing services.
1. A new information extraction task is created.
The user creates an information extraction task using the ABBYY Compreno REST API. The Control Server chooses one of the available Processing Stations and allocates the task to it. The task is then sent to the Processing Station.
2. The document is converted into the SDK’s internal format.
The Processing Station converts the document into an internal format. If any text in the document requires optical character recognition (OCR), the station uses a built-in component to recognize the text. Your license determines whether or not the OCR function is available.
3. The Processing Station performs a semantic analysis of the document.
The analysis is performed by one of the executors. To increase performance, the document may be split into parts that can be processed by other executors and Processing Stations.
4. Data is extracted from the document.
When the semantic analysis completes, information extraction rules are applied to its results. The installed Information Extraction Module determines which data extraction algorithms are applied and which entities and facts are extracted.
5. The information extraction results are saved.
The extracted entities and facts are saved to an RDF/XML file and this file is sent to the Control Server.
6. The task is completed.
The Control Server receives the RDF/XML file from the Processing Station and flags the task as completed. The user can now access the extracted entities and facts via the REST API.
Intelligence and insights
ABBYY InfoExtractor SDK takes data analysis to an entirely new level, allowing companies to take advantage of the critical facts and story lines that are, literally, right in front of their eyes. They can now harvest the true value of their information while reducing manual efforts, streamlining processes and making more informed decisions based on a deeper, context-based understanding of the data. Knowledge workers navigate directly to the relevant facts and easily retrieve the exact information they need and spend less time on searching or manual content upload.
Aid predictive decision-making
The intelligence and insights InfoExtractor provides enable business professionals to take critical decisions faster. Intelligent text analysis algorithms deliver predictable results, liberating from potential human-related mistakes. However, when it comes down to taking critical decisions, it is crucial to ensure the consistency and legitimacy of information extraction. Configurable confidence scores allow to define which results should go through human validation to ensure that no piece of business-critical information is lost.
Uncover hidden risks
Connect entities, facts and events across documents to get the big picture of relationships between persons or organizations mentioned in various pieces of content. Manage obligations across numerous contracts, leading to more control over the possible risks.
Cost efficiency
InfoExtractor allows companies to accelerate and automate content upload and analysis to optimize manual processes and therefore stay competitive with faster serving & on-boarding customers. Accelerate analysis of unstructured documents, including initial documents required for verifying new customers or transaction–related documents required for transaction legitimacy check. Customers are enrolled and receive their services faster, bringing businesses higher revenues and building reputation.
Smart Classifier supports enterprises in accessing unstructured information, turning it into an asset and using it to their advantage. No special skills required - content and process experts can set up and maintain classification.
InfoExtractor extracts critical information from unstructured data powering business tasks that require granular content analysis and understanding.
Good classification and information extraction let organisations solve tasks they are not capable of solving at the moment. Based upon Compreno, Smart Classifier and InfoExtractor both have an innovative approach and are not domain specific but can be applied in a variety of information and content management scenarios within the entire scope of an enterprise.