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Data Mining and Text-based Information Mark Wasson Senior Architect, Research Scientist LexisNexis

Data Mining and Text-based Information Mark Wasson Senior Architect, Research Scientist LexisNexis mark.wasson@lexisnexis.com August 27, 2002. The Agenda . Knowledge Discovery, Data Mining, Text Mining From Free Text to Structured Metadata Knowledge Discovery and Data Mining in Text

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Data Mining and Text-based Information Mark Wasson Senior Architect, Research Scientist LexisNexis

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  1. Data Mining and Text-based Information Mark Wasson Senior Architect, Research Scientist LexisNexis mark.wasson@lexisnexis.com August 27, 2002 Data Mining and Text-based Information - Mark Wasson

  2. The Agenda • Knowledge Discovery, Data Mining, Text Mining • From Free Text to Structured Metadata • Knowledge Discovery and Data Mining in Text • The Forecast for Data Mining and Text • Information Sources and Links Data Mining and Text-based Information - Mark Wasson

  3. Knowledge Discovery, Data Mining, Text Mining Data Mining and Text-based Information - Mark Wasson

  4. What is Knowledge Discovery? • Knowledge discovery in databases (KDD) is defined as “the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” • Stated another way, KDD is the process of applying scaled, optimized statistical processes to large quantities of structured data in order to help users discovernew, potentially interesting patterns and information in that data. Data Mining and Text-based Information - Mark Wasson

  5. What Folks Do With KDD • Find trends and patterns in current data in order to support predictions or classification as new data comes in • Explain existing data, not just describe it • Summarize the contents in a large database to facilitate decision making • Support “logical” (as opposed to graphical) data visualization to support end users Data Mining and Text-based Information - Mark Wasson

  6. What Folks Really Do With KDD • Business trends and financial instrument forecasting (e.g., predict the stock market) • Fraud detection • Merchandise handling and placement • Finding hidden relationships between entities • Credit worthiness evaluation and loan approvals • Marketing and sales data analysis • Recommender systems • Customer Relationship Management (CRM) • Bioinformatics (e.g., in silico drug discovery) • Defect identification and tracking Data Mining and Text-based Information - Mark Wasson

  7. The 9-step KDD Process • Understand application domain; determine goals • Create target dataset for analysis and discovery • Clean data for noise, missing values, etc. • Perform data reduction • Choose best data mining method to meet goals • Choose best data mining algorithm for method • Conduct data mining, i.e., apply the algorithm • Review results (novel? interesting?); redo steps if necessary • Consolidate discovered knowledge Can be fully automated, but often highly interactive Data Mining and Text-based Information - Mark Wasson

  8. What is Data Mining? (classic def’n) • A synonym for Knowledge Discovery • The statistical/analytical processing within the KDD process Data Mining and Text-based Information - Mark Wasson

  9. What Isn’t Data Mining (classic def’n) • Online Analytical Processing (OLAP) • Information Retrieval • Finding and extracting proper names and other pieces of information in a text • Document categorization and indexing • Simple descriptive statistics (e.g., average, mean, median) These tools do help find potentially interesting existing information, but not discovernew information. • Not necessarily new just because it’s new to you Data Mining and Text-based Information - Mark Wasson

  10. What is Data Mining? (buzzword) • With the emergence of successful data mining applications in the mid to late-1990s, everyone piled on to the term “data mining” • Today “data mining” is widely used to label tools and processes that • Discover new, potentially interesting information • Find existing, potentially interesting information • “Knowledge discovery” still specifically emphasizes discovery Data Mining and Text-based Information - Mark Wasson

  11. What is Text Mining? (classic def’n) • Text mining is the process of applying knowledge discovery and data mining techniques to information found in a collection of texts in order to help users discovernew, potentially interesting patterns and information in that data. • Combines information from multiple texts • What is in an individual text is known information • Authors know what they write Data Mining and Text-based Information - Mark Wasson

  12. What is Text Mining? (buzzword) • Computational linguists have piled on, too! • Today, “text mining” is widely used to label tools and processes that • Discover new, potentially interesting information in text collections • Discover new, potentially interesting information in text-based information • Find existing, potentially interesting information in text and text collections • Information Retrieval • Named Entity, Relationship and Information Extraction • Categorization and Indexing • Question Answering Data Mining and Text-based Information - Mark Wasson

  13. Today’s Key KDD Problems • Not enough focus on the data • Collection • Cleansing • Scale • Completeness, including non-traditional sources • Structure • Too much focus on algorithms • The problem of Interestingness • What is interesting? • What isn’t? • How do we tell the difference? Data Mining and Text-based Information - Mark Wasson

  14. KDD and Text Problems • We’re dealing with text! • Text lacks structure that traditional data mining processes can exploit • Information within text generally are not labeled • Actual and approximate synonymy • Ambiguity • Contrast with Spreadsheets, Databases, Etc. • Well-defined structure • Row, column headings identify content Data Mining and Text-based Information - Mark Wasson

  15. How to “Fix” Text Convert Information in Text to Metadata Data Mining and Text-based Information - Mark Wasson

  16. From Free Text to Structured Metadata Data Mining and Text-based Information - Mark Wasson

  17. What is Metadata? • Metadata is data about data • Content-based metadata is structured information that is somehow derived from the information content of a document rather than from the format of a document • Key Benefit for Data Mining: Structured representation of content • For our purposes references to “metadata” are references to content-based metadata Data Mining and Text-based Information - Mark Wasson

  18. Markup Languages and Metadata • Standard Generalized Markup Language (SGML) • Meta-language for defining markup languages • Markup primarily used to support presentation • Hypertext Markup Language (HTML) • SGML-based markup language for the web • Emphasis on structural elements of documents • Extensible Markup Language (XML) • Meta-language for defining markup languages • Markup supports both presentation and information/content identification • Ability to support information/content identification is severely limited by our ability to process text for content Data Mining and Text-based Information - Mark Wasson

  19. Content-based Metadata • Publisher-provided fields • Publication name • Title • Author • Date • Dateline • Topic-indicating terms • A list of all the words and phrases in a document • Simple list • List of unique words and phrases • Sets of related terms • Frequency information Data Mining and Text-based Information - Mark Wasson

  20. Content-based Metadata • Specialized terms • Named entities (companies, people, places, etc.) • Citations, judges, attorneys, plaintiffs, defendants • Numerical information and monetary amounts • Noun phrases and their head nouns • Sentences • Relationships • Items in close proximity • Subject-verb-object (agent-action-patient) relationships • Citation-based linkages • Coreference-based linkages (John Smith left Microsoft. He joined IBM.) Data Mining and Text-based Information - Mark Wasson

  21. Content-based Metadata • Content-indicating annotations • Controlled vocabulary indexing • Statistically interesting extracted terms • Abstracts, summaries • Specialized fields • Domain templates Data Mining and Text-based Information - Mark Wasson

  22. Value of Content-based Metadata • Search support (information finding) • Find and retrieve documents • Link to related documents • Analysis support (information understanding) • Overall content summarization • This has real value to information users • Link metadata to documents via good document IDs • Provide metadata to customers who can use it for retrieval from their own search and analysis tools Data Mining and Text-based Information - Mark Wasson

  23. Metadata Creation Technologies • Publisher-provided fields • Some basic standardization helps • Simple term listing and counting • Generally easy, and quite good • Finding Specialized Terms • Lots of good pattern recognition tools, including SRA’s NetOwl, Inxight’s ThingFinder • Pattern recognition, lexicons do well for most categories (literary titles, product names are hard) Data Mining and Text-based Information - Mark Wasson

  24. Metadata Creation Technologies • Linguistics-based lexical tools • Morphological analysis, part of speech tagging • Inxight’s LinguistX • Sentence boundary detection • Easily doable, but many need to consider more text • Linguistics-based syntactic tools • Shallow parsing • Deep parsing • Coreference resolution • Varied text, difficult but progressing Data Mining and Text-based Information - Mark Wasson

  25. Metadata Creation Technologies • Finding related items • Proximity, within sentence easy • Subject-verb-object/agent-action-patient requires some degree of parsing • Coreference-based relationship finding requires coreference resolution • SRA’s NetOwl • ClearForest’s rule books • Insightful’s InFact, SVO • Cymfony’s Brand Dashboard • Attensity, SVO • Alias I, coreference-based Data Mining and Text-based Information - Mark Wasson

  26. Metadata Creation Technologies • Template-driven extraction • Often combines many technologies into domain-specific applications • Clear Forest’s rule books • WhizBang (defunct, now Inxight?) machine learning-based extraction • Various “web-farming” technologies, e.g., Caesius • University of Sheffield’s GATE tool kit • Automatic abstracting/summarization • Leading text best for individual news documents • Columbia University’s NewsBlaster for multiple texts • True summary generation – a hard problem Data Mining and Text-based Information - Mark Wasson

  27. Metadata Creation Technologies • Document categorization and indexing • 80% - 90% accurate (recall and precision) common • Often integrated with editorial processes • Inxight • Nstein • Stratify • Verity • A lot of others Data Mining and Text-based Information - Mark Wasson

  28. Metadata Creation Technologies • Metadata creation technologies • Text mining? • Read about them • Natural Language Processing for Online Applications – Text Retrieval, Extraction and Categorization (John Benjamins Publishing Company, 2002) Peter Jackson, Vice President of R&D, and Isabelle Moulinier, Senior Research Scientist, Thomson Legal & Regulatory Data Mining and Text-based Information - Mark Wasson

  29. Knowledge Discovery and Data Mining in Text Data Mining and Text-based Information - Mark Wasson

  30. Combining KDD and Metadata • What is Knowledge Discovery in Metadata? (The term is unique to us, by the way; Ronen Feldman et al called this Knowledge Discovery in Text) • It is KDD that incorporates document metadata into its data collection step Data Mining and Text-based Information - Mark Wasson

  31. Basic KDD Task Using Metadata • Data source selection • Metadata creation, organization • Perhaps combine with other appropriate data • Align data based on common attributes • Align data based on date or time • Use knowledge sources to guide analysis of metadata (e.g., world knowledge, thesauri, etc.) • Analyze the data • Language-aware processes, e.g., SVO • Routine processes that apply to structured content Data Mining and Text-based Information - Mark Wasson

  32. Research Problems • Does document metadata have value for KDD applications in addition to its value for information finding and retrieval purposes? • If so, where? Data Mining and Text-based Information - Mark Wasson

  33. Example 1 – Trend Analysis • Research at LexisNexis • Can daily “hot topics” be identified automatically by comparing today’s indexing frequency for the topic to its recent history? • Track controlled vocabulary indexing assignments over time to determine a historical average • Compare today’s frequency of assignment for a given company’s index term to its historical average • If it exceeds some threshold, flag it as a “hot” company in that day’s news • Analysts confirmed 96.2% of 1,137 flagged companies, company pairs were in fact “hot” See Shewhart & Wasson (1999) Data Mining and Text-based Information - Mark Wasson

  34. Example 2 – Emerging Technologies • Research at IBM • Can trends in emerging and fading technologies be identified? • Extract, normalize and monitor vocabulary found in documents and compare it to document categories • Provide users with a querying tool where they can specify the “shape” of the trend • Used patent data See Lent et al. (1997) Data Mining and Text-based Information - Mark Wasson

  35. Example 3 - Influence of News Stories • Work at University of Massachusetts • Can specific news stories be identified that will influence the behavior in financial markets? • Examine features of news articles that occurred before interesting changes in the financial markets • Find patterns of features that regularly occur before interesting changes • In future data, monitor incoming stories for those patterns for alert purposes • Real-time data, real-time stock prices See Lavrenko et al. (2000) Data Mining and Text-based Information - Mark Wasson

  36. Example 4 - Citation Pattern Analysis • Can citation histories be used to identify potential relationships between specific illnesses and other features, exposures, medications, etc. • Collect the citations in a large medical texts collection • Examine citation chains in pairs of domains that do not directly cite one another • Measure the amount of overlap in the citation chain • Verify results through clinical medical research See Swanson & Smalheiser (1996) Data Mining and Text-based Information - Mark Wasson

  37. Example 5 - Sentiment Detection • Work at Webmind (out of business) • Is the tone of news stories, Usenet discussions, website stories, etc., about some company, its management or its products positive or negative? • Use categorization technology to determine the positive or negative tone in individual documents about a given company or its products • Combine results across all documents about that company or its products • Compute a score or summarize the results Data Mining and Text-based Information - Mark Wasson

  38. Example 6 - Link Genes to Diseases • Work at Hewlett Packard Laboratories • Can sets of genes be associated with given diseases by analyzing MEDLINE abstracts? • Identify references to genes, addressing major problems with recognition, ambiguity and synonymy in this domain • Identify references to targeted diseases • Statistically analyze co-occurrence patterns between mentions of the genes and mentions of diseases for statistically significant correlations See Adamic et al. (2002) Data Mining and Text-based Information - Mark Wasson

  39. Additional Examples • Analyzing the activities of a person, company or organization using its role as subject/agent or object/patient in clauses • Predicting the spread between borrowing and lending interest rates • Identifying technical traders in the T-bonds futures market • Daily predictions of major stock indexes Data Mining and Text-based Information - Mark Wasson

  40. Data Mining and Text Vendors • Alias I • Attensity • ClearForest • eNeuralNet • IBM (Intelligent Miner for Text) • Inforsense • Insightful (InFact) • Megaputer Intelligence • SAS (Enterprise Miner, Inxight) • SPSS (LexiQuest) Data Mining and Text-based Information - Mark Wasson

  41. The Forecast for Data Mining and Text Data Mining and Text-based Information - Mark Wasson

  42. What is the forecast for KDT? • Can we get information from unstructured (free) text into some structured format? • Are there enough interesting KDD applications where access to content-based metadata from text actually produces interesting results? • Does adding text-based information to existing data mining and knowledge discovery applications make them better? Data Mining and Text-based Information - Mark Wasson

  43. KDT, 1996-1999 • A handful of interesting experiments published • Mostly one-off experiments • Almost no evidence any of it was commercialized • Holding back the research • Almost no one had access to large quantities of appropriate metadata for research purposes • Linguistics technologies still maturing, often too slow • Almost no one had the combination of content and tools to generate large quantities of appropriate metadata for research purposes Data Mining and Text-based Information - Mark Wasson

  44. KDT, 2000+ • Movement. Early stages, but movement • Maturing, scaleable tools in classification and extraction from web content and other texts to create metadata • Products from the Big 3 analytical tool providers (SAS, SPSS, Insightful) • Companies created to focus on it (not always successful), such as ClearForest, Webmind • Emerging importance of bioinformatics, availability of MEDLINE content • But data mining hit hard by dot-com collapse Data Mining and Text-based Information - Mark Wasson

  45. The Forecast • KDT is emerging, but slowly • Still in early stages • Lots of promise Data Mining and Text-based Information - Mark Wasson

  46. Information Sources and Links Data Mining and Text-based Information - Mark Wasson

  47. Resources • KDnuggets, http://www.kdnuggets.com • ACM Special Interest Group in Knowledge Discovery and Data Mining, http://www.acm.org/sigkdd/ • Association for Computational Linguistics, http://www.aclweb.org • Data Mining and Knowledge Discovery (journal), Kluwer Academic Publishers, http://www.digimine.com/usama/datamine/ • Companies, http://www.kdnuggets.com/companies/ • Glossary of Terms, http://www3.shore.net/~kht/glossary.htm Data Mining and Text-based Information - Mark Wasson

  48. Related Technical Conferences • The 3rd SIAM International Conference on Data Mining, May 1-3, 2003, San Francisco, CA http://www.siam.org/meetings/sdm03/ • 2003 North American Association for Computational Linguistics/Human Language Technology Joint Conference, approx. early June, 2003, Edmonton, AB http://www.aclweb.org • The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC http://www.acm.org/sigkdd/kdd2003/ Data Mining and Text-based Information - Mark Wasson

  49. Books • Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in Knowledge Discovery and Data Mining. AAAI Press / The MIT Press. • Jackson, P., & Moulinier, I. (2002). Natural Language Processing for Online Applications – Text Retrieval, Extraction and Categorization. John Benjamins Publishing Company. Data Mining and Text-based Information - Mark Wasson

  50. Company Links Attensity, http://www.attensity.com Alias I, http://www.alias-i.com Caesius, http://www.caesius.com ClearForest, http://www.clearforest.com Columbia University, http://www.cs.columbia.edu/nlp/newsblaster/ Cymfony, http://www.cymfony.com eNeuralNet, http://www.eneuralnet.com Hewlett Packard Labs, http://www.hpl.hp.com/org/stl/dmsd/ IBM, http://www-3.ibm.com/software/data/iminer/ Data Mining and Text-based Information - Mark Wasson

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