A presentation at DH2014-Lausanne of the eMOP (at the IDHMC at Texas A&M) on our post-processing triage method along with our expanded treatment and diagnosis queues for correcting and analysing Tessearct OCR results.
The document describes the Early Modern OCR Project (eMOP) which aims to Optical Character Recognition (OCR) over 300,000 documents and 45 million page images from the Early English Books Online (EEBO) and Eighteenth Century Collections Online (ECCO) collections. It outlines the challenges of OCRing early modern texts including inconsistent fonts, layouts, and quality issues. It then details eMOP's workflow which includes training Tesseract with manually labeled glyphs, de-noising and scoring OCR output, and making corrections through the TypeWright tool. The fully-searchable OCR and corrections will be made freely available through aggregators like 18thConnect to improve access and reuse of these texts
The Early Modern OCR Project (eMOP) used funding from the Andrew W. Mellon Foundation to develop tools and techniques for applying optical character recognition (OCR) to early modern English documents from 1475-1800. eMOP aimed to improve access to these texts by making their contents fully searchable beyond just metadata or basic OCR. The project involved OCR processing over 45 million pages from EEBO and ECCO, with outcomes including improved OCR accuracy, open source tools and code, trained OCR models, a database of early modern printers, and fulltext search of the TCP EEBO transcription corpus.
A presentation at DH2014-Lausanne of the Tesseract training methods and tools developed by eMOP (at the IDHMC at Texas A&M), and their uses for other book history and typeface history research.
The Early Modern OCR Project (eMOP) aims to improve the optical character recognition (OCR) of over 300,000 documents and 45 million pages from the Early English Books Online and Eighteenth Century Collections Online databases dating from 1475-1800. The project develops techniques to train OCR engines on the specific typefaces in these collections. Undergraduate students create ground truth data to train Tesseract, while tools like Aletheia and Franken+ standardize the process. Documents undergo processing, triage, treatment and correction. The resulting OCR and page tags will be made available through tools like TypeWright to allow scholars to search and correct texts.
The document discusses the Early Modern OCR Project (eMOP) which aims to improve access to early modern texts from 1475-1800 by applying optical character recognition (OCR) to make the text fully searchable. Key points:
- eMOP is funded by the Mellon Foundation and run out of Texas A&M University. It develops open-source tools to apply OCR to collections like EEBO and ECCO containing over 45 million page images.
- Challenges of OCRing early modern texts include worn type, layout variations, and image quality issues. Results show 68% accuracy for EEBO and 86% for ECCO using Tesseract OCR.
- The project has
Digital Frontiers 2015: eMOP's Imprint (Printer's and Publisher's) DBMatt Christy
The Early Modern OCR Project (eMOP) is developing an imprint database of early modern English printers and publishers between 1475-1800 using text extracted from documents. eMOP receives Andrew W. Mellon Foundation funding and is run out of Texas A&M University. The project aims to make the contents of over 300,000 documents and 45 million pages fully searchable by improving optical character recognition on these historical texts.
The Early Modern OCR Project (eMOP) uses big data techniques to digitize over 45 million page images from Early English Books Online and Eighteenth Century Collections Online. eMOP partners with several universities and labs to perform OCR and analyze the text. The project faces challenges in digitizing Early Modern printed works due to issues like worn typefaces, page layout variations, and image quality problems. eMOP utilizes various workflows managed through a dashboard to process the collections using the Brazos High Performance Computing Cluster.
The document describes the Early Modern OCR Project (eMOP) which aims to Optical Character Recognition (OCR) over 300,000 documents and 45 million page images from the Early English Books Online (EEBO) and Eighteenth Century Collections Online (ECCO) collections. It outlines the challenges of OCRing early modern texts including inconsistent fonts, layouts, and quality issues. It then details eMOP's workflow which includes training Tesseract with manually labeled glyphs, de-noising and scoring OCR output, and making corrections through the TypeWright tool. The fully-searchable OCR and corrections will be made freely available through aggregators like 18thConnect to improve access and reuse of these texts
The Early Modern OCR Project (eMOP) used funding from the Andrew W. Mellon Foundation to develop tools and techniques for applying optical character recognition (OCR) to early modern English documents from 1475-1800. eMOP aimed to improve access to these texts by making their contents fully searchable beyond just metadata or basic OCR. The project involved OCR processing over 45 million pages from EEBO and ECCO, with outcomes including improved OCR accuracy, open source tools and code, trained OCR models, a database of early modern printers, and fulltext search of the TCP EEBO transcription corpus.
A presentation at DH2014-Lausanne of the Tesseract training methods and tools developed by eMOP (at the IDHMC at Texas A&M), and their uses for other book history and typeface history research.
The Early Modern OCR Project (eMOP) aims to improve the optical character recognition (OCR) of over 300,000 documents and 45 million pages from the Early English Books Online and Eighteenth Century Collections Online databases dating from 1475-1800. The project develops techniques to train OCR engines on the specific typefaces in these collections. Undergraduate students create ground truth data to train Tesseract, while tools like Aletheia and Franken+ standardize the process. Documents undergo processing, triage, treatment and correction. The resulting OCR and page tags will be made available through tools like TypeWright to allow scholars to search and correct texts.
The document discusses the Early Modern OCR Project (eMOP) which aims to improve access to early modern texts from 1475-1800 by applying optical character recognition (OCR) to make the text fully searchable. Key points:
- eMOP is funded by the Mellon Foundation and run out of Texas A&M University. It develops open-source tools to apply OCR to collections like EEBO and ECCO containing over 45 million page images.
- Challenges of OCRing early modern texts include worn type, layout variations, and image quality issues. Results show 68% accuracy for EEBO and 86% for ECCO using Tesseract OCR.
- The project has
Digital Frontiers 2015: eMOP's Imprint (Printer's and Publisher's) DBMatt Christy
The Early Modern OCR Project (eMOP) is developing an imprint database of early modern English printers and publishers between 1475-1800 using text extracted from documents. eMOP receives Andrew W. Mellon Foundation funding and is run out of Texas A&M University. The project aims to make the contents of over 300,000 documents and 45 million pages fully searchable by improving optical character recognition on these historical texts.
The Early Modern OCR Project (eMOP) uses big data techniques to digitize over 45 million page images from Early English Books Online and Eighteenth Century Collections Online. eMOP partners with several universities and labs to perform OCR and analyze the text. The project faces challenges in digitizing Early Modern printed works due to issues like worn typefaces, page layout variations, and image quality problems. eMOP utilizes various workflows managed through a dashboard to process the collections using the Brazos High Performance Computing Cluster.
SAA 2014 Pre-conference Workshop - OCRing with Open Source ToolsMatt Christy
A Society of American Archivists 2014 Annual Conference, Pre-conference Workshop describing the open-source tools and workflows used/created by the Early Modern OCR Project to improve the OCR of early modern printed documents
This document discusses practical aspects of natural language processing (NLP) work. It contrasts research work, which involves setting goals, devising algorithms, training models, and testing accuracy, with development work, which focuses on implementing algorithms as scalable APIs. The document emphasizes that obtaining data is crucial for NLP and describes sources for structured, semi-structured, and unstructured data. It recommends Lisp as a language that supports the interactivity, flexibility, and tree processing needed for NLP research and development work.
This document provides an overview of natural language processing (NLP) including the linguistic basis of NLP, common NLP problems and approaches, sources of NLP data, and steps to develop an NLP system. It discusses tokenization, part-of-speech tagging, parsing, machine learning approaches like naive Bayes classification and dependency parsing, measuring word similarity, and distributional semantics. The document also provides advice on going from research to production systems and notes areas not covered like machine translation and deep learning methods.
This document summarizes Suneel Marthi's presentation on large scale natural language processing. It discusses how natural language processing deals with processing and analyzing large amounts of human language data using computers. It provides an overview of Apache OpenNLP and Apache Flink, two open source projects for natural language processing. It also discusses how models for tasks like part-of-speech tagging and named entity recognition can be trained for different languages and integrated into data pipelines for large scale processing using these frameworks.
Big Data Spain 2017 - Deriving Actionable Insights from High Volume Media St...Apache OpenNLP
Media analysts have to deal with with analyzing high volumes of real-time news feeds and social media streams which is often a tedious process because they need to write search profiles for entities. Python tools like NLTK do not scale to large production data sets and cannot be plugged into a distributed scalable frameworks like Apache Flink. Apache Flink being a streaming first engine is ideally suited for ingesting multiple streams of news feeds, social media, blogs etc.. and for being able to do streaming analytics on the various feeds. Natural Language Processing tools like Apache OpenNLP can be plugged into Flink streaming pipelines so as to be able to perform common NLP tasks like Named Entity Recognition (NER), Chunking, and text classification. In this talk, we’ll be building a real-time media analyzer which does Named Entity Recognition (NER) on the individual incoming streams, calculates the co-occurrences of the named entities and aggregates them across multiple streams; index the results into a search engine and being able to query the results for actionable insights. We’ll also be showing as to how to handle multilingual documents for calculating co-occurrences. NLP practitioners will come away from this talk with a better understanding of how the various Apache OpenNLP components can help in processing large streams of data feeds and can easily be plugged into a highly scalable and distributed framework like Apache Flink.
n overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
Embracing diversity searching over multiple languagesSuneel Marthi
This document discusses multi-lingual search and machine translation. It introduces Tommaso Teofili and Suneel Marthi, who work on Apache projects related to natural language processing. They discuss why multi-lingual search is important to embrace diversity online. Statistical machine translation generates translations from models trained on parallel text corpora. Phrase-based models can translate phrases as units and handle reordering better than word-based models. Apache Joshua is an open source machine translation decoder used by many organizations.
This document provides an overview of natural language processing (NLP) including popular NLP problems, levels of NLP, the role of linguistics, sources of NLP data, tools and algorithms used in NLP, types of models including language models, and considerations for building practical NLP systems. It also describes a practical example of building a language detection system using word language models trained on Wiktionary data and evaluated using Wikipedia test data.
What one needs to know to work in Natural Language Processing field and the aspects of developing an NLP project using the example of a system to identify text language
Crash Course in Natural Language Processing (2016)Vsevolod Dyomkin
This document provides an overview of natural language processing (NLP) including:
1. An introduction to NLP and its intersection with computational linguistics, computer science, and statistics.
2. A discussion of common NLP problems like tokenization, tagging, parsing, and their rule-based and statistical approaches.
3. An explanation of machine learning techniques for NLP like language models, naive Bayes classifiers, and dependency parsing.
4. Steps for developing an NLP system including translating requirements, experimentation, and going to production.
An overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
An overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
Platforms can be abstract, workbench-based, or dedicated. Abstract platforms support ideas and conceptual modeling. Workbench platforms provide tools and materials to build applications. Dedicated platforms focus on specific tasks. A case study showed how conceptual graphs and an analogy engine helped analyze legacy software and documentation by identifying files, variables, similarities, and discrepancies. Semantic web technologies could contribute to platform interoperability.
This document provides an overview of hands-on tasks for a link discovery tutorial using the Limes framework. It describes a test dataset, and three tasks: 1) executing a provided Limes configuration to detect duplicate authors, 2) creating a configuration to find similar publications based on keywords, and 3) using the Limes GUI.
The document provides an overview of text mining presented by Yi-Shin Chen. It discusses preprocessing text data which includes language detection, removing noise, stemming, POS tagging, and other techniques. It then covers basic concepts in natural language processing and parsing. Finally, it introduces basic data modeling concepts like the entity-relationship model and its use of entities, attributes, and relationships to represent data.
This document provides a quick tour of data mining. It begins with an overview of the evolution of data management techniques from manual record keeping to modern big data and data science. It then discusses what data mining is, focusing on algorithms for discovering patterns in existing data. Various examples of data mining applications are also presented, as well as the origins of data mining in fields such as machine learning and databases. Finally, an overview of the key steps in the knowledge discovery process is given, including data preprocessing, data mining, and pattern evaluation.
An overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
This document discusses research into automatically discovering strong relationships between entities in Linked Data using genetic programming. The researchers aim to learn a cost function that can guide uninformed searches over Linked Data to find the most promising relationship paths. They experiment with different topological and semantic features as inputs to genetic programming to learn cost functions. The best-performing cost functions incorporate features like namespace variety, conditional node degree, and topics. This suggests specific, well-described paths through entities of different types are indicators of strong relationships in Linked Data.
1) The document discusses optimizing large genome assembly by using stateful continuous bulk processing (CBP) on the Azure cloud platform. CBP allows efficient stateful graph processing and avoids reprocessing unchanged data.
2) The approach involves porting an existing genome assembly pipeline called Contrail to use CBP. Contrail currently uses Hadoop and MapReduce for genome assembly but is inefficient and slow.
3) Using CBP on Azure and a provenance manager called Newt, the ported pipeline can trace data and provenance through multi-stage processing, replay actors on selected inputs, and handle errors like crashes transparently through state management without full reprocessing.
SAA 2014 Pre-conference Workshop - OCRing with Open Source ToolsMatt Christy
A Society of American Archivists 2014 Annual Conference, Pre-conference Workshop describing the open-source tools and workflows used/created by the Early Modern OCR Project to improve the OCR of early modern printed documents
This document discusses practical aspects of natural language processing (NLP) work. It contrasts research work, which involves setting goals, devising algorithms, training models, and testing accuracy, with development work, which focuses on implementing algorithms as scalable APIs. The document emphasizes that obtaining data is crucial for NLP and describes sources for structured, semi-structured, and unstructured data. It recommends Lisp as a language that supports the interactivity, flexibility, and tree processing needed for NLP research and development work.
This document provides an overview of natural language processing (NLP) including the linguistic basis of NLP, common NLP problems and approaches, sources of NLP data, and steps to develop an NLP system. It discusses tokenization, part-of-speech tagging, parsing, machine learning approaches like naive Bayes classification and dependency parsing, measuring word similarity, and distributional semantics. The document also provides advice on going from research to production systems and notes areas not covered like machine translation and deep learning methods.
This document summarizes Suneel Marthi's presentation on large scale natural language processing. It discusses how natural language processing deals with processing and analyzing large amounts of human language data using computers. It provides an overview of Apache OpenNLP and Apache Flink, two open source projects for natural language processing. It also discusses how models for tasks like part-of-speech tagging and named entity recognition can be trained for different languages and integrated into data pipelines for large scale processing using these frameworks.
Big Data Spain 2017 - Deriving Actionable Insights from High Volume Media St...Apache OpenNLP
Media analysts have to deal with with analyzing high volumes of real-time news feeds and social media streams which is often a tedious process because they need to write search profiles for entities. Python tools like NLTK do not scale to large production data sets and cannot be plugged into a distributed scalable frameworks like Apache Flink. Apache Flink being a streaming first engine is ideally suited for ingesting multiple streams of news feeds, social media, blogs etc.. and for being able to do streaming analytics on the various feeds. Natural Language Processing tools like Apache OpenNLP can be plugged into Flink streaming pipelines so as to be able to perform common NLP tasks like Named Entity Recognition (NER), Chunking, and text classification. In this talk, we’ll be building a real-time media analyzer which does Named Entity Recognition (NER) on the individual incoming streams, calculates the co-occurrences of the named entities and aggregates them across multiple streams; index the results into a search engine and being able to query the results for actionable insights. We’ll also be showing as to how to handle multilingual documents for calculating co-occurrences. NLP practitioners will come away from this talk with a better understanding of how the various Apache OpenNLP components can help in processing large streams of data feeds and can easily be plugged into a highly scalable and distributed framework like Apache Flink.
n overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
Embracing diversity searching over multiple languagesSuneel Marthi
This document discusses multi-lingual search and machine translation. It introduces Tommaso Teofili and Suneel Marthi, who work on Apache projects related to natural language processing. They discuss why multi-lingual search is important to embrace diversity online. Statistical machine translation generates translations from models trained on parallel text corpora. Phrase-based models can translate phrases as units and handle reordering better than word-based models. Apache Joshua is an open source machine translation decoder used by many organizations.
This document provides an overview of natural language processing (NLP) including popular NLP problems, levels of NLP, the role of linguistics, sources of NLP data, tools and algorithms used in NLP, types of models including language models, and considerations for building practical NLP systems. It also describes a practical example of building a language detection system using word language models trained on Wiktionary data and evaluated using Wikipedia test data.
What one needs to know to work in Natural Language Processing field and the aspects of developing an NLP project using the example of a system to identify text language
Crash Course in Natural Language Processing (2016)Vsevolod Dyomkin
This document provides an overview of natural language processing (NLP) including:
1. An introduction to NLP and its intersection with computational linguistics, computer science, and statistics.
2. A discussion of common NLP problems like tokenization, tagging, parsing, and their rule-based and statistical approaches.
3. An explanation of machine learning techniques for NLP like language models, naive Bayes classifiers, and dependency parsing.
4. Steps for developing an NLP system including translating requirements, experimentation, and going to production.
An overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
An overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
Platforms can be abstract, workbench-based, or dedicated. Abstract platforms support ideas and conceptual modeling. Workbench platforms provide tools and materials to build applications. Dedicated platforms focus on specific tasks. A case study showed how conceptual graphs and an analogy engine helped analyze legacy software and documentation by identifying files, variables, similarities, and discrepancies. Semantic web technologies could contribute to platform interoperability.
This document provides an overview of hands-on tasks for a link discovery tutorial using the Limes framework. It describes a test dataset, and three tasks: 1) executing a provided Limes configuration to detect duplicate authors, 2) creating a configuration to find similar publications based on keywords, and 3) using the Limes GUI.
The document provides an overview of text mining presented by Yi-Shin Chen. It discusses preprocessing text data which includes language detection, removing noise, stemming, POS tagging, and other techniques. It then covers basic concepts in natural language processing and parsing. Finally, it introduces basic data modeling concepts like the entity-relationship model and its use of entities, attributes, and relationships to represent data.
This document provides a quick tour of data mining. It begins with an overview of the evolution of data management techniques from manual record keeping to modern big data and data science. It then discusses what data mining is, focusing on algorithms for discovering patterns in existing data. Various examples of data mining applications are also presented, as well as the origins of data mining in fields such as machine learning and databases. Finally, an overview of the key steps in the knowledge discovery process is given, including data preprocessing, data mining, and pattern evaluation.
An overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
This document discusses research into automatically discovering strong relationships between entities in Linked Data using genetic programming. The researchers aim to learn a cost function that can guide uninformed searches over Linked Data to find the most promising relationship paths. They experiment with different topological and semantic features as inputs to genetic programming to learn cost functions. The best-performing cost functions incorporate features like namespace variety, conditional node degree, and topics. This suggests specific, well-described paths through entities of different types are indicators of strong relationships in Linked Data.
1) The document discusses optimizing large genome assembly by using stateful continuous bulk processing (CBP) on the Azure cloud platform. CBP allows efficient stateful graph processing and avoids reprocessing unchanged data.
2) The approach involves porting an existing genome assembly pipeline called Contrail to use CBP. Contrail currently uses Hadoop and MapReduce for genome assembly but is inefficient and slow.
3) Using CBP on Azure and a provenance manager called Newt, the ported pipeline can trace data and provenance through multi-stage processing, replay actors on selected inputs, and handle errors like crashes transparently through state management without full reprocessing.
The document discusses three main problems with de novo assembly of next generation sequencing data and proposes solutions. The three problems are 1) large memory and compute requirements for assembly, 2) complexity of the assembly process and lack of standardized protocols, and 3) limited training opportunities that are difficult for students. The proposed solutions are standardized assembly protocols called khmer-protocols that provide copy-paste workflows for mRNAseq and metagenome assembly using techniques like digital normalization to reduce memory usage and make assembly scalable. The khmer-protocols are designed to be open, versioned, and reproducible to generate initial assembly results cheaply and easily in the cloud.
Using MXNet Gluon, we outline a pipeline for Optical Character Recognition that uses a CNN for page detection, SSD (Single Shot Multibox Detection) for line detection and CNN-BiLSTM with CTC Loss for handwriting recognition. We also investigate extensions to improve performance with Lexicon Search and Language Modelling.
The document summarizes a distributed architecture system for recognizing textual entailment. The system uses a peer-to-peer network with caching mechanisms to improve speed. It transforms text and hypotheses into dependency trees using tools like LingPipe and MINIPAR. Resources like DIRT, WordNet, Wikipedia and an acronym database are used to calculate the fitness between text and hypothesis for determining entailment. The system achieved third place in the 2007 RTE competition with an accuracy of 69.13%.
This document discusses using decipherment techniques to improve machine translation when parallel data is scarce. It presents an overview of machine translation pipelines and notes that performance drops when parallel data is limited. The document proposes using monolingual data to improve machine translation in real-world scenarios with limited parallel data. It outlines contributions including fast, accurate decipherment of over 1 billion tokens with 93% accuracy, and using decipherment to improve machine translation for domain adaptation and low-resource languages.
The document discusses various approaches to natural language processing (NLP) problems, including preprocessing text data, traditional machine learning models, deep learning models, and word embeddings. It covers preprocessing steps like removing spaces, tokenization, spelling correction, stemming, handling stopwords. It also discusses using TF-IDF features and latent semantic analysis with SVD for classification models. Finally, it discusses using word embeddings to represent text as vectors for sequence models.
Natural language processing and transformer modelsDing Li
The document discusses several approaches for text classification using machine learning algorithms:
1. Count the frequency of individual words in tweets and sum for each tweet to create feature vectors for classification models like regression. However, this loses some word context information.
2. Use Bayes' rule and calculate word probabilities conditioned on class to perform naive Bayes classification. Laplacian smoothing is used to handle zero probabilities.
3. Incorporate word n-grams and context by calculating word probabilities within n-gram contexts rather than independently. This captures more linguistic information than the first two approaches.
This document introduces khmer, a platform for scalable sequence analysis. It discusses how khmer uses k-mers to provide implicit read alignments and assemble sequences using de Bruijn graphs. It also describes some of the challenges with k-mers, such as each sequencing error resulting in novel k-mers. The document outlines khmer's data structures and algorithms for efficiently counting k-mers and represents de Bruijn graphs. It discusses how khmer has been applied to real biological problems and highlights areas of current research using khmer, such as error correction, variant calling, and assembly-free comparisons of data sets.
This document provides an overview of text analysis with Python. It discusses common text mining tasks like entity recognition, sentiment analysis, and categorization. It promotes Python for short, concise text processing using tools like NLTK, Scipy, NumPy, and Scikit-learn. Key pre-processing steps covered include lowercasing, tokenization, handling entities, and removing stopwords. Supervised classification techniques like Naive Bayes classifiers and maximum entropy models are explained. Examples presented include a gender classifier and a system for recognizing questions in tweets.
This document outlines a 12-step program for biology to adapt to the era of data-intensive science. It summarizes the author's background and research interests. It then discusses the rapid growth of biological data from techniques like DNA sequencing. It introduces the concept of digital normalization as a way to efficiently process large transcriptome datasets. Finally, it outlines some proposed steps for the field, including investing in computational training, a focus on biological questions, and moving to continuous data updating models.
Machine Reading Using Neural Machines (talk at Microsoft Research Faculty Sum...Isabelle Augenstein
The document discusses machine reading using neural machines. It presents goals of fact checking claims and understanding scientific publications. It outlines challenges in tasks like stance detection on tweets and summarizing scientific papers. These include interpreting statements based on the target or headline, handling unseen targets, and the small size of benchmark datasets which makes neural machine reading computationally costly.
The document summarizes a presentation given by Chris Fregly on end-to-end real-time analytics using Apache Spark. It discusses topics like Spark streaming, machine learning, tuning Spark for performance, and demonstrates live demos of sorting, matrix multiplication, and thread synchronization optimized for CPU cache. The presentation emphasizes techniques like cache-friendly data layouts, prefetching, and lock-free algorithms to improve Spark performance.
ER1 Eduard Barbu - EXPERT Summer School - Malaga 2015RIILP
The document discusses collecting and cleaning multilingual data. It describes estimating the amount of parallel data that exists in Common Crawl, testing different crawlers, and developing a machine learning approach to classify translation units as either true translations or errors. Key points include estimating that Common Crawl contains around 1 billion parallel pages, crawlers tested had low recall, and the best performing model for classifying translation units was an SVM classifier with an F1-score of 0.81.
Oana Tifrea-Marciuska's research focuses on artificial intelligence for social good, specifically how people reason and communicate. Her work includes developing tools for automatic humor recognition, adaptive learning systems, personalized search incorporating social and semantic data, fact checking, and semantic parsing of natural language questions into logical forms. She has published papers on representing and reasoning with preferences in ontology languages like Datalog± to enable more personalized search and query answering over semantic data.
Beyond the Symbols: A 30-minute Overview of NLPMENGSAYLOEM1
This presentation delves into the world of Natural Language Processing (NLP), exploring its goal to make human language understandable to machines. The complexities of language, such as ambiguity and complex structures, are highlighted as major challenges. The talk underscores the evolution of NLP through deep learning methodologies, leading to a new era defined by large-scale language models. However, obstacles like low-resource languages and ethical issues including bias and hallucination are acknowledged as enduring challenges in the field. Overall, the presentation provides a condensed, yet comprehensive view of NLP's accomplishments and ongoing hurdles.
Querying the Cairo Genizah Images with Word-Spotting Algorithm (En)
Adiel Ben-Shalom, Prof. Yaacov Choueka, The Friedberg Genizah Project, Prof. Nachum Dershowitz, Prof. Lior Wolf, Tel Aviv University
Querying the Cairo Genizah Images with Word-Spotting Algorithm (En)
Adiel Ben-Shalom, Prof. Yaacov Choueka, The Friedberg Genizah Project, Prof. Nachum Dershowitz, Prof. Lior Wolf, Tel Aviv University
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
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mchristy-Dh2014- emop-postOCR-triage
1. eMOP Post-OCR Triage
Diagnosing Page Image Problems with Post-OCR
Triage for eMOP
Matthew Christy,
Loretta Auvil,
Dr. Ricardo Gutierrez-
Osuna,
Boris Capitanu,
Anshul Gupta,
Elizabeth Grumbach
2. emop.tamu.edu/
DH2014 Presentation
emop.tamu.edu/post-
processing
eMOP Workflows
emop.tamu.edu/workflow
s
Mellon Grant Proposal
idhmc.tamu.edu/projects
/Mellon/eMOPPublic.pdf
eMOP Info
eMOP Website More eMOP
Facebook
The Early Modern OCR
Project
Twitter
#emop
@IDHMC_Nexus
@matt_christy
@EMGrumbach
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
2
3. The Numbers
Page Images
Early English Books online
(Proquest) EEBO: ~125,000
documents, ~13 million
pages images (1475-1700)
Eighteenth Century
Collections Online (Gale
Cengage) ECCO: ~182,000
documents, ~32 million
page images (1700-1800)
Total: >300,000 documents
& 45 million page images.
Ground Truth
Text Creation Partnership TCP:
~46,000 double-keyed hand
transcribed docuemnts
44,000 EEBO
2,200 ECCO
DH2014 - Book History and Software Tools: Examining Typefaces for OCR Training in eMOP
3
4. Page Images
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
4
5. The Constraints
45 million page images!
Only 2 years
Small IDHMC team focused
on gather data and training
Tesseract for early modern
typefaces
Great team of collaborators
focusing on post-processing
Software Environment for the
Advancement of Scholarly
Research (SEASR) – University of
Illinois, Urbana-Champaign
Perception, Sensing, and
Instrumentation (PSI) Lab, Texas
A&M University
Everything must be open-
source
Focus our efforts on post-
processing triage and
recovery
Triage system will score page
results and route pages to be
corrected or analyzed for
problems
Results:
1. Good quality, corrected
OCR output
2. A DB of tagged pages
indicating pre-processing
needs
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
5
Solution
8. Triage:De-noising
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
8
Uses hOCR results
1. Determine average
word bounding box
size
2. Weed out boxes are
that too big or too
small
3. But keep small boxes
that have neighbors
that are “words”
9. Triage: De-noising
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
9
Before: 35% After: 58%
11. Triage: Estimated Correctability
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
11
Page Evaluation
Determine how correctable a
page’s OCR results are by
examining the text.
The score is based on the ratio
of words that fit the
correctable profile to the total
number of words
Correctable Profile
1. Clean tokens:
remove leading and trailing
punctuation
remaining token must have at
least 3 letters
2. Spell check tokens >1
character
3. Check token profile :
contain at most 2 non-alpha
characters, and
at least 1 alpha character,
have a length of at least 3,
and do not contain 4 or more
repeated characters in a run
4. Also consider length of tokens
compared to average for the
page
12. DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
12Triage: Estimated Correctability
13. Treatment: Page Correction
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
13
1. Preliminary cleanup
remove punctuation from begin/end of
tokens
remove empty lines and empty tokens
combine hyphenated tokens that appear
at the end of a line
retain cleaned & original tokens as
“suggestions”
2. Apply common transformations and
period specific dictionary lookups to
gather suggestions for words.
transformation rules: rn->m; c->e; 1->l; e
14. DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
14Treatment: Page Correction
3. Use context checking on a sliding window of 3 words,
and their suggested changes, to find the best context
matches in our(sanitized, period-specific) Google 3-
gram dataset
if no context is found and only one additional suggestion
was made from transformation or dictionary, then
replace with this suggestion
if no context and “clean” token from above is in the
dictionary, replace with this token
15. DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
15Treatment: Page Correction
window: tbat l thoughc
Candidates used for context matching:
tbat -> Set(thai, thar, bat, twat, tibet, ébat, ibat, tobit, that, tat, tba, ilial,
abat, tbat, teat)
l -> Set(l)
thoughc -> Set(thoughc, thought, though)
ContextMatch: that l thought (matchCount: 1844 , volCount: 1474)
window: l thoughc Ihe
Candidates used for context matching:
l -> Set(l)
thoughc -> Set(thoughc, thought, though)
Ihe -> Set(che, sho, enc, ile, iee, plie, ihe, ire, ike, she, ife, ide, ibo, i.e,
ene, ice, inc, tho, ime, ite, ive, the)
ContextMatch: l though the (matchCount: 497 , volCount: 486)
ContextMatch: l thought she (matchCount: 1538 , volCount: 997)
ContextMatch: l thought the (matchCount: 2496 , volCount: 1905)
tbat I thoughc Ihe Was
16. DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
16Treatment: Page Correction
window: thoughc Ihe Was
Candidates used for context matching:
thoughc -> Set(thoughc, thought, though)
Ihe -> Set(che, sho, enc, ile, iee, plie, ihe, ire, ike, she, ife, ide, ibo, i.e,
ene, ice, inc, tho, ime, ite, ive, the)
Was -> Set(Was)
ContextMatch: though ice was (matchCount: 121 , volCount: 120)
ContextMatch: though ike was (matchCount: 65 , volCount: 59)
ContextMatch: though she was (matchCount: 556,763 , volCount:
364,965)
ContextMatch: though the was (matchCount: 197 , volCount: 196)
ContextMatch: thought ice was (matchCount: 45 , volCount: 45)
ContextMatch: thought ike was (matchCount: 112 , volCount: 108)
ContextMatch: thought she was (matchCount: 549,531 , volCount:
325,822)
ContextMatch: thought the was (matchCount: 91 , volCount: 91)
that I thought she was
19. Diagnosis: Page Tagging
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
19
Tags pages with
problems that prevent
good OCR results
Can be used to apply
appropriate pre-
processing and re-
OCRing
Eventually, will end up
with a list of pages that
simply need to be re-
digitized
This will be the first time
any comprehensive
analysis has been done
on these page images.
Users tag sample pages in
a desktop version of Picasa
Machine learning
algorithms use those tags
to learn how to recognize
skew, warp, noise, etc.
Have developed
algorithms to:
measure skew
measure noise
20. Further/Current Work
Identifying multiple pages/columns in an image
Predicting juxta scores for documents without
corresponding groundtruth
Identifying warp
Identify and fixing incorrect word order in hOCR
output
can occur on pages with skew, vertical lines,
decorative drop-caps, etc.
will affect scoring and context-based corrections
Develop measure of noisiness
Develop measure of skew-ness
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
20
21. The end
For eMOP questions please
contact us at :
mchristy@tamu.edu
egrumbac@tamu.edu
DH2014 - Diagnosing Page Image Problems with Post-OCR Triage for eMOP
21
Editor's Notes
The Early Modern OCR Project (eMOP) is an Andrew W. Mellon Foundation funded grant project running out of the Initiative for Digital Humanities, Media, and Culture (IDHMC) at Texas A&M University, to develop and test tools and techniques to apply Optical Character Recognition (OCR) to early modern English documents from the hand press period, roughly 1475-1800. The basic premise of eMOP is to use typeface and book history techniques to train modern OCR engines specifically on the typefaces in our collection of documents, and thereby improve the accuracy of the OCR results. eMOP’s immediate goal is to make machine readable, or improve the readability, for 45 million pages of text from two major proprietary databases: Eighteenth Century Collections Online (ECCO) and Early English Books Online (EEBO). Generally, eMOP aims to improve the visibility of early modern texts by making their contents fully searchable. The current paradigm of searching special collections for early modern materials by either metadata alone or “dirty” OCR is inefficient for scholarly research.
Some were great
most were not
Noisy
Skewed
Warped
Or they posed challenges for OCR engines
Multiple pages per image
Multiple columns
Images & decorative elements
Marginalia
Missing margins
many were terrible
CONSTRAINTS:
We knew there were plenty of pre-processing algorithms to solve many of these problems, but given these constraints we felt we couldn’t conceivably pre-process all pages with all algorithms.
SOLUTION:
By making our triage system more robust we could attempt to correct as much as possible, but also identify page image problems and tag each page in the DB so that we’d know what pre-processing should be applied in order to get better results when re-OCRing again later.
Before: 55%
After: 73%
This will be the first time that any sort of comprehensive analysis has been done on the page images of these collections.