Text Analytics Forum
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The inaugural Text Analytics Forum (TAF) was a definite success!

The conference was developed and chaired by Tom Reamy, Chief Knowledge Architect of the KAPS Group and long term text analtyics consultant. The two initial keynotes filled the room to overflowing and we continued with a good sized crowd for the two days - good sized and quite enthusiastic.

The program began with two keynotes. The first was a general welcome by the chair and a presentation of some of the current themes in text analytics:

  • AI and Deep Learning - will it make humans obsolete or empowered
  • Machine Learning vs. Rules-based
  • Poly-structured text - Content Types and Section
  • New Knowledge Structures - Cognitive and Social

The second keynote by long time market expert, Seth Grimes, presented an overview of the current and future text analytics market - who are the winners and losers, the cutting edge developments and how to stay abreast of future developments

After the keynotes, we split into two tracks - a technical track and a business/applications track. By the second day, it was clear that the technical track was the clear favorite - a message for future conferences. The first day ended with a very popular features, an Ask the Experts Panel - four experts answering some prepared questions but mostly fielding questiions from the audience. This segment was so popular we had to push people out so they could attend the welcome reception.
    Our Four Experts
  • Jeremy Bentley- Smartlogic
  • Bryan Bell - Expert System
  • Jeff Fried - BA Insight
  • Michael Upshall - UNSILO

We asked one prepared question and then opened it up for questions from the audience and and hour later we still had multiple hands up for more when we had to quit. The dialog was both informative and a lot of fun.

To spice it up even more, we had a contest to select the best audience question with the winner getting a free copy of my book, Deep Text. I made the selection and thought I did a good job until it turned out that the winner had already bought and read her own copy.

The biggest complaint was that the talks were too short - something we will fix for next year's conference. Speaking of which, next year's conference is set for Nov. 7 and 8. Once again it will be co-located with KMWorld, Taxonomy Boot Camp, Enterprise Search and Discovery and more. See the Info Today list of conferencesfor updates.

Themes of the Technical Track

AI and Text Analytics
  • AI-based analytics systems can successfully be adopted and merged
  • with appropriate business practices
  • AI Vs. Automation
Cognitive Computing and Graph Databases
  • Graph stores combined with Text Analytics
  • NLP and Entity Extractors for cognitive computing and semantic graph databases
Machine Learning, Taxonomy, and Search
  • Combining machine learning, text analytics, and semantic Web for automated tagging
  • The Saviour Machine: Text Analytics, Machine Learning, and the Role of Taxonomy
Machine Learning Vs. Rules
  • Automatic Classification: Rules-based vs training-set-based bakeoff
  • Text Analytics and Machine Learning - Case Study
Auto-Categorization and Summarization
  • Auto Categorization by Taxonomy: Pros, Cons and Pragmatics
  • Search, Semantic Analysis, Text Mining - New Approach
Text and Data Together
  • Extracting Content for Linked Data Triples
Measuring Results
  • Measuring Auto-categorization Quality
  • Information Retrieval Performance Measurement Using Extrapolated Precision

Themes of the Application Track

Case Studies
  • Building text analysis models to understand and predict adverse events occurring with FDA-approved drugs
  • A New Way of Working - Graph and Semantics; Text Analytics and Linked Data
Search and Text Analytics
  • Leveraging Text Analytics to Build a Personalized Information Retrieval Environment
  • Using Text Analytics, Taxonomy and Search to Probe Ignorance and Risk
Fake News and Bad Ad Placement
  • News Analytics system
  • Content meets Interest - Contextual Ad Targeting by means of cognitive computing
Case Studies II - Banks and Publishing
  • Text Analytics and KM
  • Machine Learning in practice
Taxonomy and Text Analytics
  • Bringing it all together (at last): Integrating Structured and Unstructured Information with Text Analytics and Ontologies
  • Taxonomies and Text Analytics - Recent Projects
New Applications
  • Human-like Semantic Reasoning
  • Breaking Down Silos with Text Analytics
Application Issues
  • Leveraging Text Analytics to Build Applications
  • Maximizing analytic value from multi-language text feeds

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