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- Optional Workshop/Conference Day
- Optional Conference Day
- TA Development
- Technical
- Tools & Techniques
- AI & Rules
- Monday Nov 15
- Tuesday Nov 16
- Wednesday Nov 17
- Thursday Nov 18
Optional Workshop/Conference Day
Optional Conference Day
TA Development
Technical
Tools & Techniques
AI & Rules
9:00 AM
Optional Workshop/Conference Day
Length: 8 Hours
Description: Text Analytics Forum 2021 is a part of a unique program of four co-located conferences this November — Text Analytics Forum,KMWorld,Taxonomy Boot Camp, andEnterprise Search & Discovery. Please take an opportunity to explore these events and their content, then choose a Platinum Pass to gain full access to these distinct, but synergistic, conferences.
5:00 PM
Optional Workshop/Conference Day
Length: 1 Hour 30 Minutes
Description: Join us for the Enterprise Solutions Showcase Grand Opening reception. Explore the latest products and services from the top companies in the marketplace while enjoying small bites and drink. Open to all conference attendees, speakers, and sponsors.
8:30 AM
Optional Conference Day
Length: 8 Hour 30 Minutes
Description: Text Analytics Forum 2021 is a part of a unique program of four co-located conferences this November — Text Analytics Forum,KMWorld,Taxonomy Boot Camp, andEnterprise Search & Discovery. Please take an opportunity to explore these events and their content, then choose a Platinum to gain full access to these distinct, but synergistic, conferences.
5:00 PM
Happy Hour in the Enterprise Solutions Showcase
Length: 1 Hour
8:00 AM
8:30 AM
10:00 AM
Coffee Break in the Enterprise Solutions Showcase
Length: 45 Minutes
10:45 AM
Keynotes
Length: 45 Minutes
Speaker(s):
Tom Reamy, Chief Knowledge Architect,KAPS Group, LLC, USA
Tom Reamy, Chief Knowledge Architect,KAPS Group, LLC, USA
Description: What are the current and future trends for the field of text analytics? Join program chair Tom Reamy for an overview of the conference themes and highlights and a look at what is driving the field forward. This year’s main theme is the mutual enrichment of text analytics and AI as expressed in a growing variety of applications. Knowledge graphs continue to produce great applications. We also continue the exploration of machine learning and rules-based approaches and how people are combining them to get the best of both worlds. The talk wraps up with a look at current and future trends that promise to dramatically enhance our ability to utilize text with new techniques and applications.
11:45 AM
TA Development
Length: 45 Minutes
Speaker(s):
Seth Grimes, Principal Consultant,Alta Plana Corporation
Seth Grimes, Principal Consultant,Alta Plana Corporation
Title: Recent Advances in Natural Language Processing
Time: 11:45 AM - 12:30 PM
Description: Text analytics relies on natural language processing (NLP), powered by machine learning, semantic, and real-world knowledge systems. The NLP state of the art is advancing rapidly. This talk brings you up-to-date on recent years' advances in distributional models, vector-spaces embeddings, representations, and transformers: BERT (and its descendants), GPT-3, and all that. We discuss special tasks such as question-answering, conversational systems, natural language generation, and emotion AI. Naturally you'll want to know how, so we look at leading open-source and commercial NLP options and touch on data, bias, and ethics concerns. In sum, this talk takes a comprehensive look at what works in NLP and what to expect in days to come.
Technical
Length: 45 Minutes
Speaker(s):
Christian Fluhr, Scientific Director,GEOLSemantics
Aurelie Rossi, Team Development Director,GEOL Semantics
Christian Fluhr, Scientific Director,GEOLSemantics
Aurelie Rossi, Team Development Director,GEOL Semantics
Title: Alert Detection Generating for Critical Geo-Chronolocated Events
Time: 11:45 AM - 12:30 PM
Description: In response to tragic events, GEOLSemantics offers its technology to launch rescue missions and other interventions through generating alerts for critical geo-chronolocated events. This combined technological solution (mixing NLP and AI) can collect information on past, current, or future events that are identified through an ontology about disasters, information collection, and security, as well as accidents and other critical incidents. It is currently being successfully applied in a Smart City project to monitor threats, hazards, and critical events. The system monitors traditional and social media platforms and can locate and date events. It aims to extract information on important events from the time the first related message appears and before the events are trending. It can assign an accurate position by using metadata and elements from messages. Social media sources call for automatic speech transcription when dealing with videos and social network languages influenced by internet slang. In this context, NLP, and more precisely NLU, is essential to overcome language ambiguities and properly identify critical dates, locations, and events. The solution covers critical alert monitoring through geo-chronolocated events such as accidents, fires hazards, flooding, and other disasters, as well as violence and spontaneous events. An event involves three elements: action, place, time. By using an ontology combined with extraction rules, each element can be extracted based on the semantics, rather than just a pattern, so the information related to the event can be enriched. GEOLSemantics converts information gleaned from text into geographic coordinates by using a GIS database to locate events on a map.
12:30 PM
Attendee Lunch in the Enterprise Solutions Showcase
Length: 1 Hour
1:30 PM
TA Development
Length: 45 Minutes
Speaker(s):
Ari Kramer, Knowledge Management Officer,Robert Wood Johnson Foundation
Tom Reamy, Chief Knowledge Architect,KAPS Group, LLC, USA
Ari Kramer, Knowledge Management Officer,Robert Wood Johnson Foundation
Tom Reamy, Chief Knowledge Architect,KAPS Group, LLC, USA
Title: Introducing Auto-Classification at a Major National Foundation
Time: 1:30 PM - 2:15 PM
Description: In fall 2019, the Robert Wood Johnson Foundation (RWJF) released a new enterprise taxonomy model for classification of grants and key information related to its grantmaking. Since rollout, a critical next step has been the introduction of auto-classification to augment RWJF's current manual taxonomy application process. To begin this effort, RWJF initiated a comprehensive text analytics software evaluation that led to a working prototype for the Topics facet of its taxonomy and used it to successfully reprocess and retag a large volume of grants that suffered from past over-indexing. The evaluation included a POC that built autocategorization rules which, utilizing a content structure model, achieved 95%-plus accuracy. These rules formed the basis for the first application. Informed by this effort, RWJF is now in the initial stages of integrating text analytics into grant-related applications and preparing to extend the configuration to additional taxonomy facets so it can begin moving forward with auto-classification. This includes getting staff ready for the changes and exploring a growing range of potential use cases. Join RWJF's knowledge management officer, Ari Kramer, and Tom Reamy of KAPS Group for a discussion about the approach RWJF is taking to auto-classification, key challenges, and lessons learned, as well as how RWJF is planning to leverage text analytics in the near- and long-term.
Technical
Length: 45 Minutes
Speaker(s):
Joseph Hilger, COO,Enterprise Knowledge LLC
Sara Nash, Senior Technical Analyst,Enterprise Knowledge, LLC
Helmut Nagy, COO,Semantic Web Company
Joseph Hilger, COO,Enterprise Knowledge LLC
Sara Nash, Senior Technical Analyst,Enterprise Knowledge, LLC
Helmut Nagy, COO,Semantic Web Company
Title: Knowledge Graphs—The New Model to Integrate Text & Data
Time: 1:30 PM - 2:15 PM
Description: Organizations recognize that their internal data and content assets are key to establishing enterprise AI initiatives, making their internal workforce more efficient and more effective in serving their sector. However, they often feel restricted by stubborn legacy systems and old-school approaches to modeling and utilizing assets. Knowledge graphs offer a new approach to tackle the challenge of integrating text and data. Leveraging structured data sources to describe unstructured text enables better classification and discovery of that content. Conversely, extracting semantic meaning from unstructured content to enrich structured data makes data more accessible and prepared for a variety of use cases. This talk discusses the best practices for semantic data modeling and architecture design, including how to establish a use case, select core technologies, iteratively enrich your knowledge graph, and apply the model to downstream applications.
Title: Semantic AI—How a Knowledge Graph Brings Quality to Machine Learning
Time: 1:30 PM - 2:15 PM
Description: We all know that machine learning algorithms can only learn from historical data, but they cannot derive new insights from it. We also know that when implementing explainable AI (XAI) systems, it is crucial to make their decisions explainable and transparent, incorporating new conditions and regulatory frameworks quickly. It's in the nature of machine learning algorithms that the basis of their calculated rules cannot be explained; they are just “a matter of fact.” Including knowledge graphs as a prerequisite to calculate not only rules, but also corresponding explanations can be a solution to this. A semantic AI architecture is based on machine learning as well as knowledge graphs, where data analysts and knowledge scientists work together, making use of a knowledge graph to directly extract data that can be quickly transformed into structures for analysis. The results of the analyses themselves can then be reused to enrich the knowledge graph. The semantic AI approach thus creates a continuous cycle in which both machine learning and knowledge scientists play an integral part. Knowledge graphs act as an interface in between, providing high-quality linked and normalized data This talk outlines the building blocks of a semantic AI architecture and shows some concrete examples.
2:30 PM
TA Development
Length: 45 Minutes
Speaker(s):
Alison Bushell, Head of Global Analytics,Forsta
Fang Chen, Principal Analytics Consultant,Forsta
Alison Bushell, Head of Global Analytics,Forsta
Fang Chen, Principal Analytics Consultant,Forsta
Title: From Data Collection to Action: High-Performing Experience Text Analytics at Work
Time: 2:30 PM - 3:15 PM
Description: Most businesses and organizations realize the importance of understanding their constituents' (stakeholders, employees, customers, consumers, clients, etc.) experiences and opinions. Fewer, however, have a deep understanding of how to integrate speech and text information into their strategic decision making. Forsta shares its experience by breaking down the flow of information and identifying the best practices and traps in each step. Bushell covers solicited and unsolicited sources, storage and organization, preprocessing and categorization, and analysis and taking actions. She shows you how to tackle each step effectively and efficiently so you get the most out of your experience data, reduce users' decision-making risk, and increase your analytics cred.
Technical
Length: 45 Minutes
Speaker(s):
Jans Aasman, CEO,Franz Inc.
Jans Aasman, CEO,Franz Inc.
Title: Graph Neural Networks for Text Classification & Relation Extraction
Time: 2:30 PM - 3:15 PM
Description: Enterprises are subscribed to the power of modeling data as a graph and the importance of building knowledge graphs for customer 360 and beyond. The ability to explain the results of AI models and produce consistent results from them involves modeling real-world events with the adaptive schema consistently provided via knowledge graphs. Graph neural networks (GNNs) have emerged as a mature AI approach used by companies for knowledge graph enrichment via text processing for news classification, question and answer, search result organization, and much more. A graph can represent many things—social media networks, patient data, contracts, drug molecules, etc. GNNs enhance neural network methods by processing the graph data through rounds of message passing; as such, the nodes know more about their own features as well as neighbor nodes. This creates an even more accurate representation of the entire graph network. This presentation discusses the advantages of GNNs for text classification and relationship extraction.
3:15 PM
Coffee Break in the Enterprise Solutions Showcase
Length: 45 Minutes
4:00 PM
TA Development
Length: 1 Hour
Description: A panel of four text analytics experts answers questions that have been gathered before and during the conference, as well as some additional questions from the program chair. This has been one of our most popular features in previous years, so come prepared with your favorite questions and be ready to learn.
8:00 AM
8:30 AM
10:00 AM
10:15 AM
AI & Rules
Length: 45 Minutes
Speaker(s):
Walid Saba, Founder and Principal AI Scientist,ontologik.ai
Walid Saba, Founder and Principal AI Scientist,ontologik.ai
Title: Natural Language & Text Analytics: Limitations of & Alternatives to the Data-Driven & Machine Learning Methods
Time: 10:15 AM - 11:00 AM
Description: Data-driven, statistical, and machine learning (ML) approaches are the currently dominant paradigm in the use of natural language processing (NLP) in text analytics. This talk discusses the limitations of the data-driven approach, particularly in tasks such as sentiment analysis, text filtering, and media monitoring. We argue that these methods can produce results that are, at best, probably, approximately, correct. Moreover, these methods are not scalable, as they require continuous training on massive amounts of data that are often not available. Instead, we argue for a semantic counter-revolution, where deep semantic analysis as well as ontological knowledge repositories are employed. As part of this, a brief description of the semantic method is presented with a discussion of actual use cases in knowledge management and e-discovery.
11:15 AM
Tools & Techniques
Length: 45 Minutes
Title: Text Analytics for Non-Textual Assets
Time: 11:15 AM - 12:00 PM
Description: Digital assets such as video, audio recordings, and still images are rich sources for content analysis. However, in order to apply text analytics methods, we need to rely on textual representations of these non-textual objects. This session discusses how we can leverage machine-generated transcripts and human-entered metadata as the basis of analysis. It looks at how we can build an annotation pipeline using APIs and services as well as at some outcomes and lessons learned from a business use
Title: Low-Image Quality Documents—A Hybrid Approach to Automation
Time: 11:15 AM - 12:00 PM
Description: Manual redaction of sensitive information within historical document images is an important and mind-numbing task. In the past, automation was confounded by poor image quality and high document variability. The challenge stems from the spectrum of quality of the source documents, where the biggest concerns lie with those that are not digitally born. From this aspect, one must develop a robust mechanism for grading the source document for quality as well as an infrastructure to house the document components for feature or rule development to occur. Given the wide array of document types that could be consumed, it is from this stage that classification can take place utilizing purpose-built models to understand what next steps need to be taken. Finally, from this stage, we can employ other rule sets or models to achieve the task of processing automation. This presentation explores these various stages to provide insights into lessons learned and the approaches that were found that provided the greatest benefits.
12:00 PM
Tools & Techniques
Length: 1 Hour
Description: Check back soon for keynote details.
1:00 PM
Tools & Techniques
Length: 45 Minutes
Speaker(s):
Kyle S. Strand, Senior Knowledge Management Specialist,Inter-American Development Bank
Kyle S. Strand, Senior Knowledge Management Specialist,Inter-American Development Bank
Title: Mapping Employee Knowledge: A Comparison of Two Word-Embedding Algorithms
Time: 1:00 PM - 1:45 PM
Description: The Inter-American Development Bank is a multilateral development institution with a mission to work with governments and other actors to address development challenges in Latin America and the Caribbean. This mission motivates the Bank to be constantly strengthening its capacity by generating and acquiring knowledge from its operations in 27 countries, as well as from external sources. These efforts have included using deep learning to create a multilingual language model to map employee expertise and contextualize user queries for improved relevance of search results. Still, even in this focused field, there are numerous tools that can be used. This presentation will compare performance using a word-level vector-embeddings algorithm (word2vec) and a character-level vector-embeddings algorithm (fasttext) to power search for people within the organization and reflect on future applications.
2:00 PM
Tools & Techniques
Length: 45 Minutes
Speaker(s):
Heather Hedden, Data and Knowledge Engineer,Semantic Web Company
Phillip Peng, Director of Applied AI,Walden University
Heather Hedden, Data and Knowledge Engineer,Semantic Web Company
Phillip Peng, Director of Applied AI,Walden University
Title: Taxonomies & Text Analytics for Recommendation Systems
Time: 2:00 PM - 2:45 PM
Description: Recommendation systems go beyond the limitations for search to get the right information to the right people by providing suggestions, such as for content, products, opportunities, connections, etc. A knowledge-based recommendation system, making use of a knowledge graph and text analytics, has advantages over other recommender technologies. This session presents a prototype recommendation system, an "HR Recommender" (for jobs, projects, and people to connect to), and explains how it is built. It is based on a semantic model of taxonomies and an ontology, content that is text-mined, algorithms for calculating similarities, a search index, and a front-end user interface. A demo of the recommender application and a demo of a tool for text mining and taxonomy/ontology modeling behind it are presented.
Title: An NPS Survey Analysis Powered With Natural Language Processing Algorithms
Time: 2:00 PM - 2:45 PM
Description: Today's corporate organizations have widely adopted the Net Promoter Score (NPS) survey as a tool to assist in measuring and better understanding overall customer satisfaction as well as brand health. However, limited insights have been drawn from the survey text comments beyond the calculated numerical NPS score. Attempts of manually encoding the NPS survey text comments suffer from the drawbacks of time-consuming, inconsistent categorization of topics and sentiments. Text analytics enables companies to gold-mine rich insights from the open-ended free-text comments. We use advanced NLP (natural language processing) algorithms to leverage the voice of customers. We first use a topic modeling algorithm to categorize each comment. Second, we run an aspect-based sentiment analysis, as one comment may cover multiple topics. Lastly, we join the NLP data (topics and sentiment scores) with customers' attribute data and conduct a clustering analysis to create customer segments to understand who, what, and how they feel about the company's products and services. This NPS survey analysis, powered with advanced NLP algorithms, generates actionable insights for the business to improve customer satisfaction and brand health.
3:00 PM
Tools & Techniques
Length: 45 Minutes
Title: Early Detection of Emerging Trends
Time: 3:00 PM - 3:45 PM
Description: Medical library science specialists, voice-of-customer program managers, politicians, financial and insurance managers, competitive intelligence analysts—all could benefit from early detection of emerging trends. However, timely discovery of important new issues is not an easy task. It requires processing huge amounts of text data to identify previously unknown but growing trends, which frequently reveal themselves as a weak signal on the background of strong noise of already-known issues. We discuss the technological foundation and the methodology for deploying a system that automates early detection of new issues. Also, we provide a live demonstration of a system that automates the discovery of emerging trends for a pharma company. Then we discuss the benefits provided by this system, as well as typical organizational challenges encountered during the implementation.
Title: T5 – A Swiss Army Knife for Many Text Analytics/NLP Tasks?
Time: 3:00 PM - 3:45 PM
Description: The deep learning sequence architecture of the open-domain T5 model from Google gives us an easy way of implementing a number of natural language processing (NLP) tasks with a minimum of training data. As such, T5 is like a Swiss army knife, with multiple blades that can be easily adapted and configured to perform a variety of tasks. This talk describes T5 and demonstrates its use on several analytics tasks such as text classification, question answering, and data augmentation.
4:00 PM
