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Text Mining with D2K/T2K

Text Mining with D2K/T2K. Outline. Text Mining Brief Intro Unsupervised Supervised Information Extraction … ALG Technology Pieces Demonstrations Discussion. What is text mining?.

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Text Mining with D2K/T2K

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  1. Text Mining with D2K/T2K

  2. Outline • Text Mining Brief Intro • Unsupervised • Supervised • Information Extraction • … • ALG Technology Pieces • Demonstrations • Discussion

  3. What is text mining? • In simplified and practical terms it is the extraction of a relatively small amount of information of interest from a mass amount of text data. But … • You might not know what you’re looking for. • Discovering patterns in the haystack. (clustering, mining associations) • How to recognize a needle. • Sifting through the haystack. (model building, supervised learning) • Just the facts please. • Enumerating the make and model of every needle. (information extraction)

  4. Common Tasks for Text Mining & Analysis • Information retrieval • Automatic grouping (clustering) of documents • (Active) Classification • Information extraction • Topic detection and tracking • Automatic summarization • “Understanding” text and question answering • Machine Translation

  5. Text Preprocessing • Preprocessing (Text -> Numeric Representation) • Tokenization • Sentence Splitting • Part-of-Speech Tagging • Term Normalization (Stemming) • Filtering (Stops) • Chunking • Term Extraction • Filtering (Again) • Term Weighting • Other Transformations • Resource Taxing

  6. Clustering: Document Self-Organization Weakly Similar Arcs Broken Strongly Similar Arcs Kept • Agglomerative (bottom up) • Quadratic time complexity • Sampling • Random • Partition • Hard vs. Soft • Unsupervised method • Basic notion to all of these approaches is some heuristic for measuring similarity between documents and document groups (term co-occurrence)

  7. How to Recognize a Needle • To classify your data you often need to build a model. • To build a model you typically need examples from a “teacher” – metaphorically speaking. • Finding good examples can be hard. • T2K can also use active learning to help find good examples faster making model building easier.

  8. Pattern Mining • Finding frequent item sets -> Rule Discovery • Many methods: Apriori, Charm, FPGrowth, CLOSET • Working with Jiawei Han and students -- Hwanjo Yu and Xiaolei Li • Application: topic tree construction

  9. Just the Facts Please • Finding a document that has the information you need is often not the end goal. • To extract information you must first recognize it – you need to build a model, and that means you need to have examples. • Levels of IE: What’s hard and what’s harder?

  10. D2K

  11. D2K Overview D2K Features • Extension of existing API • Provides the capability to programmatically connect modules and set properties. • Allows D2K-driven applications to be developed. • Provides ability to pause and restart an itinerary. • Enhanced Distributed Computing • Allows modules that are re-entrant to be executed remotely. • Uses Jini services to look up distributed resources. • Includes interface for specifying the runtime layout of a distributed itinerary. • Processor Status Overlay • Shows utilization of distributed computing resources. • Distributed Checkpointing • Resource Manager • Provides a mechanism for treating selected data structures as if they were stored in global memory. • Provides memory space that is accessible from multiple modules running locally as well as remotely. • Batch Processing / Web Services

  12. D2K/T2K/I2K - Data, Text, and Image Analysis Information Visualization

  13. The Technology Pieces Applications Toolkit I2K Core Modules T2K D2K Engine • The Engine (distributed, parallelized, persistent) • Core Modules (building blocks) • T2K is a specialized set of modules for text analysis • I2K is a specialized set of modules for image analysis • D2K Toolkit (rapid development environment) • ThemeWeaver is an independent application that uses the D2K engine to run algorithms constructed from T2K modules. It is a demonstration platform • Other D2K driven applications (StreamLined, EMO, …)

  14. T2K Core 1.0 (Beta) T2K Core • Tokenization • POS Tagging • Stemming • Chunking • Filters • Term Weighting • Supervised / Unsupervised Learning • GATE Integration • Pattern Mining • Text Streams • Summarization

  15. ThemeWeaver

  16. ThemeWeaver: Prototype Text Clustering Application • Hard clustering algorithms • Modified Kmeans (3 sampling methods) • Soft clustering • Suffix tree based algorithm • Can be used for longer documents • Visualizations • “Single link” graph representation • Dendogram cluster tree • Clusters over time • Drill down and backtrack UI • D2K/T2K Driven

  17. Staff Loretta Auvil Peter Bajcsy Colleen Bushell Dora Cai David Clutter Lisa Gatzke Vered Goren Chris Navarro Greg Pape Tom Redman Duane Searsmith Andrew Shirk Anca Suvaiala David Tcheng Michael Welge Students Tyler Alumbaugh Bradley Berkin Jacob Biehl John Cassel Peter Groves Olubanji Iyun Sang-Chul Lee Young-Jin Lee Xiaolei Li Brian Navarro Scott Ramon Sunayana Saha Martin Urban Bei Yu Hwanjo Yu The ALG Team

  18. * Demo / Discussion *

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