The Artificial Intelligence Conference on Search, Data and Text Mining, Analytics and Visualization.

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Dr. Haxel Consult

Start Your New Year In A Positive and Hopeful Way There is something about the beginning of a New Year that fills me with even more optimism than usual. I can’t deny that 2020 was challenging on many levels for most people around the world and me – and for our last Conference. And as an experienced Conference organizer who believes in the power of virtual and physical networking to help direct our thoughts in positive ways, I decided to go on with our Conference in 2021. The show must go on – physically or online. Please take that on board and make your positive plans for 2021. The call for papers is open and Early Bird Exhibition booking with special prices is possible … more @ www.ai-sdv.com

Artificial Intelligence Conference on
Search, Data and Text Mining, Analytics and Visualization
www.haxel.com
©ChristophHaxel
4–6 OCTOBER . NICE, FRANCE
AI-SDV 2021

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It is relatively easy for a human to read a document and quickly figure out which concepts are important. However, this task is a difficult challenge for a machine. During the past few decades, there have been two main approaches for concept identification: Natural Language Processing and Machine Learning. During the early part of this century, Machine Learning made great strides as new techniques came into wider use (SVM’s, Topic Modeling, etc..). Sensing the competition, Natural Language Processing responded with deployment of new emerging techniques (sematic networks, finite state automata, etc..). Neither approach has completely solved the WHAT problem. Advances in Artificial Intelligence have the potential to significantly improve the situation. Where AI is making the most impact is as an enhancement to make Machine Learning and Natural Language Processing work better and, more importantly, work together. This presentation looks at some of this history and what might happen in the future when we blend the interpretation of language with pattern prediction.

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Trademarks serve as key leading indicators for innovation and economic growth. As the vanguards of new and expanding enterprises, trademarks can be used to study entrepreneurship and shifting market demands in response to varying economic factors. This responsiveness has been seen as recently as the COVID-19 pandemic, where trademark research revealed key insights about business reaction to the global upheaval. At CIPO, we have been delving more deeply than ever before into trademark analysis by leveraging cutting-edge natural language processing (NLP) tools to derive actionable business intelligence from trademark data. In this presentation, we present a survey of NLP in use at CIPO and the insights we have learned applying them. These insights include COVID-19 responses, line-of-business trends based on firm characteristics, and more. We also discuss ongoing and future trademark research projects at CIPO. These projects include emerging technology detection methods and high-resolution trademark classification systems. We conclude that artificial intelligence-enhanced tools like NLP are key components of future exploitation of trademark data for business and economic intelligence.

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In our customer projects involving automated document processing, we often encounter document types providing crucial data in the form of tables. While established text analytics algorithms are usually optimized to operate on running text, they tend to produce rather poor results on tables as they do not capture the non-sequential relations inside them (e.g. interpret the content of a table cell relative to its column title, interpret line breaks inside a cell differently from line breaks between cells or rows). While there are elaborate information extraction products in the market for a few highly specific types of tabular documents, there is no general approach out there. The main cause for this is the fact that table structures can be encoded by a heterogenous range of layout means (e.g. column boundaries can be signaled by lines vs. aligned text vs. white space). In this talk, we will illustrate several solutions that we have developed for a range of challenges occurring in this context, both for scanned and digitally generated documents.

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Most scientific journals request, that the complete set of research data is published simultaneously with the peer-reviewed paper. The publication of the research data usually is carried out as so-called "Supplementary Material", attached to the original paper, or on a "Research Data Repository". Both forms have in common, that the data is published usually unstructured and not in an uniform machine processable format. This makes its further use in electronic tools for AI or data mining unnecessarily difficult or even impossible. A concept is presented, in which the data is digitally recorded, following the principle of FAIR data, as part of the publication process. This digital capture makes the data available to the scientific community for easy use in data mining and AI tools. The data in the repository contains links to the publication to document its origin. The concept is applicable for preprints, peer-review papers, diploma and doctoral theses and is particularly suitable for open access publications. Moreover, the presentation highlights correspondent activities, which were released in scientific publications recently.

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AI-SDV 2022 The Meeting AI-SDV 2022 Conference and Exhibition is planned to be held physically and virtually. Speakers, attendees and exhibitors are invited to join physically and virtually in Vienna, Austria. We are developing a range of digital formats that respond precisely to the speakers, attendees and exhibitors needs, enabling individuals and exhibitors from AI industry and related sectors worldwide to participate in the AI-SDV 2022 in Vienna. The Artificial Intelligence Conference on Search, Data and Text Mining, Analytics and Visualization. The 2022 AI-SDV Conference in Vienna, Austria, 10th - 11th October 2022. AI-SDV 2022 is the place to be for everyone involved in advanced search and data applications, text mining and visualization technologies. Individuals and companies that are shaping the future of this exciting space that surrounds us and impacts us all will present their latest research findings, tech developments and visions for the future. Enjoy a full-on two days of learning, networking and exploring technologies and concepts that are state of the art and will continue to change way we as individuals and organisations work, rest and play. The focus is on Artificial Intelligence (AI), Digitization 2.0 (about making companies, processes and people ready for AI), Deep Learning and other topics identified by our community, who are specialists working in scientific and technical information. The event features approximately 22 speakers over two days, plus an exhibition to complementing the conference programme. As past attendees tell us, Vienna is a fabulous location and beyond the formal meetings taking place in the conference and exhibition space, we make the most of this gorgeous location for informal evening receptions and networking dinners.

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Synonym breaks search! How? Why is this important? What synonym is and how it breaks search will be explained with real-world examples. AI-based solutions are proposed, and relevant standards are identified. How synonym solutions should be used for search are explained. Learn what you can do yourself. Tools help, but it doesn’t have to be complicated, nor expensive. It is as straight forward as setting priorities!

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New tools are indeed present promising opportunities. However, the use of AI also raises new concerns regarding the transparency of the expected usability and reliability. This presentation will elaborate around some the associated questions; the impact of the training data, the black box symptoms, verification of performance and privacy considerations when using AI. But of course also some insights to best practice of AI search tool and how to take control of the process for better efficiency.

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What do PLI, MetOpera, ASCO, and PLOS have in common? Content management and content discovery needed major improvements. User were not getting the results they needed. The content production team including editorials, managing editorials – the whole team – could no longer cope with the volume and variety. Content quality was suffering. Brief discussions of each organization’s challenges set the stage for AI-based, human curated solutions. What worked, what didn’t, and the how and the why will be presented.

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The use of machine learning in IP activities has increased exponentially over the past five years. At the same time new tools, methods and systems have begun to emerge that seek to make the analysis of patent data easier to accomplish using these techniques. Included in these new developments are a significant number of machine learning systems that have begun coming to market. As these changes continue to occur, it would be useful to review some of the tools, systems, or methods that a patent practitioner has at their disposal. Examples and perspectives on the latest advances in machine learning for IP will be provided. There will also be a tour of ML4Patents.com which is devoted to aggregating content associated with the development of this area.

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The EXTRA classifier is a scalable solution based on recent advances in Natural Language Processing (NLP). The foundational concept of the EXTRA classifier is transfer learning, a machine learning process that enables the relatively low-cost specialization of a pre-trained language model to a specific task in a specific domain with far fewer training examples compared to standard machine learning solutions. More specifically, the EXTRA classifier leverages BERT, a well-known pre-trained autoencoding language model that has revolutionized the NLP space in the past few years. BERT provides contextual embeddings, i.e., it provides context-aware vector representations of words that capture semantics far more efficiently than their context-free counterparts. The EXTRA classifier contains a pre-processing module to cope with the inevitable noise in the output of standard Optical Character Recognition systems. The pre-processed plain text from a source document is then fed into a BERT-based classifier, which is built by extending pre-trained BERT with an additional linear layer trained for classification through a process commonly known as fine-tuning. We will present preliminary results that confirm some clear benefits with respect to rule-based solutions in terms of classification performance and system scalability.

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• The rapidly growing amount of patent documentation will soon no longer be manageable with conventional search methods - possibly this is already the case today. • For a couple of years now, the application of machine learning (ML) methods are being discussed as a potential solution for reducing human effort in searching large amounts of patent data. While some promising projects and ideas in this direction have been presented by different sources, the real breakthrough for ML as a standard and widely accepted patent search method has not happened yet. • The presentation looks into the challenges that still exist in this area, especially as far as practical applicability and acceptance by users is concerned, using the Intergator Smart Search project as an example.

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The Artificial Intelligence Conference on Search, Data and Text Mining, Analytics and Visualization.

1. Artificial Intelligence Conference on Search, Data and Text Mining, Analytics and Visualization www.haxel.com ©ChristophHaxel 4–6 OCTOBER . NICE, FRANCE AI-SDV 2021