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August 27, 2002Data Mining and Text-based Information - Mark Wasson 1 Data Mining and Text-based Information Mark Wasson Senior Architect, Research Scientist.

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Presentation on theme: "August 27, 2002Data Mining and Text-based Information - Mark Wasson 1 Data Mining and Text-based Information Mark Wasson Senior Architect, Research Scientist."— Presentation transcript:

1 August 27, 2002Data Mining and Text-based Information - Mark Wasson 1 Data Mining and Text-based Information Mark Wasson Senior Architect, Research Scientist LexisNexis August 27, 2002

2 Data Mining and Text-based Information - Mark Wasson 2 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 The Agenda

3 August 27, 2002Data Mining and Text-based Information - Mark Wasson 3 Knowledge Discovery, Data Mining, Text Mining

4 August 27, 2002Data Mining and Text-based Information - Mark Wasson 4 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 discover new, potentially interesting patterns and information in that data. What is Knowledge Discovery?

5 August 27, 2002Data Mining and Text-based Information - Mark Wasson 5 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 What Folks Do With KDD

6 August 27, 2002Data Mining and Text-based Information - Mark Wasson 6 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 What Folks Really Do With KDD

7 August 27, 2002Data Mining and Text-based Information - Mark Wasson 7 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 The 9-step KDD Process

8 August 27, 2002Data Mining and Text-based Information - Mark Wasson 8 A synonym for Knowledge Discovery The statistical/analytical processing within the KDD process What is Data Mining? (classic defn)

9 August 27, 2002Data Mining and Text-based Information - Mark Wasson 9 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 discover new information. –Not necessarily new just because its new to you What Isnt Data Mining (classic defn)

10 August 27, 2002Data Mining and Text-based Information - Mark Wasson 10 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 What is Data Mining? (buzzword)

11 August 27, 2002Data Mining and Text-based Information - Mark Wasson 11 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 discover new, 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 What is Text Mining? (classic defn)

12 August 27, 2002Data Mining and Text-based Information - Mark Wasson 12 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 What is Text Mining? (buzzword)

13 August 27, 2002Data Mining and Text-based Information - Mark Wasson 13 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 isnt? –How do we tell the difference? Todays Key KDD Problems

14 August 27, 2002Data Mining and Text-based Information - Mark Wasson 14 Were 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 KDD and Text Problems

15 August 27, 2002Data Mining and Text-based Information - Mark Wasson 15 Convert Information in Text to Metadata How to Fix Text

16 August 27, 2002Data Mining and Text-based Information - Mark Wasson 16 From Free Text to Structured Metadata

17 August 27, 2002Data Mining and Text-based Information - Mark Wasson 17 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 What is Metadata?

18 August 27, 2002Data Mining and Text-based Information - Mark Wasson 18 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 Markup Languages and Metadata

19 August 27, 2002Data Mining and Text-based Information - Mark Wasson 19 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 Content-based Metadata

20 August 27, 2002Data Mining and Text-based Information - Mark Wasson 20 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.) Content-based Metadata

21 August 27, 2002Data Mining and Text-based Information - Mark Wasson 21 Content-indicating annotations –Controlled vocabulary indexing –Statistically interesting extracted terms –Abstracts, summaries –Specialized fields –Domain templates Content-based Metadata

22 August 27, 2002Data Mining and Text-based Information - Mark Wasson 22 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 Value of Content-based Metadata

23 August 27, 2002Data Mining and Text-based Information - Mark Wasson 23 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 SRAs NetOwl, Inxights ThingFinder –Pattern recognition, lexicons do well for most categories (literary titles, product names are hard) Metadata Creation Technologies

24 August 27, 2002Data Mining and Text-based Information - Mark Wasson 24 Linguistics-based lexical tools –Morphological analysis, part of speech tagging –Inxights 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 Metadata Creation Technologies

25 August 27, 2002Data Mining and Text-based Information - Mark Wasson 25 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 –SRAs NetOwl –ClearForests rule books –Insightfuls InFact, SVO –Cymfonys Brand Dashboard –Attensity, SVO –Alias I, coreference-based Metadata Creation Technologies

26 August 27, 2002Data Mining and Text-based Information - Mark Wasson 26 Template-driven extraction –Often combines many technologies into domain-specific applications –Clear Forests rule books –WhizBang (defunct, now Inxight?) machine learning- based extraction –Various web-farming technologies, e.g., Caesius –University of Sheffields GATE tool kit Automatic abstracting/summarization –Leading text best for individual news documents –Columbia Universitys NewsBlaster for multiple texts –True summary generation – a hard problem Metadata Creation Technologies

27 August 27, 2002Data Mining and Text-based Information - Mark Wasson 27 Document categorization and indexing –80% - 90% accurate (recall and precision) common –Often integrated with editorial processes –Inxight –Nstein –Stratify –Verity –A lot of others Metadata Creation Technologies

28 August 27, 2002Data Mining and Text-based Information - Mark Wasson 28 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 Metadata Creation Technologies

29 August 27, 2002Data Mining and Text-based Information - Mark Wasson 29 Knowledge Discovery and Data Mining in Text

30 August 27, 2002Data Mining and Text-based Information - Mark Wasson 30 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 Combining KDD and Metadata

31 August 27, 2002Data Mining and Text-based Information - Mark Wasson 31 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 Basic KDD Task Using Metadata

32 August 27, 2002Data Mining and Text-based Information - Mark Wasson 32 Does document metadata have value for KDD applications in addition to its value for information finding and retrieval purposes? If so, where? Research Problems

33 August 27, 2002Data Mining and Text-based Information - Mark Wasson 33 Research at LexisNexis Can daily hot topics be identified automatically by comparing todays indexing frequency for the topic to its recent history? –Track controlled vocabulary indexing assignments over time to determine a historical average –Compare todays frequency of assignment for a given companys index term to its historical average –If it exceeds some threshold, flag it as a hot company in that days news –Analysts confirmed 96.2% of 1,137 flagged companies, company pairs were in fact hot See Shewhart & Wasson (1999) Example 1 – Trend Analysis

34 August 27, 2002Data Mining and Text-based Information - Mark Wasson 34 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) Example 2 – Emerging Technologies

35 August 27, 2002Data Mining and Text-based Information - Mark Wasson 35 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) Example 3 - Influence of News Stories

36 August 27, 2002Data Mining and Text-based Information - Mark Wasson 36 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) Example 4 - Citation Pattern Analysis

37 August 27, 2002Data Mining and Text-based Information - Mark Wasson 37 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 Example 5 - Sentiment Detection

38 August 27, 2002Data Mining and Text-based Information - Mark Wasson 38 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) Example 6 - Link Genes to Diseases

39 August 27, 2002Data Mining and Text-based Information - Mark Wasson 39 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 Additional Examples

40 August 27, 2002Data Mining and Text-based Information - Mark Wasson 40 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 Vendors

41 August 27, 2002Data Mining and Text-based Information - Mark Wasson 41 The Forecast for Data Mining and Text

42 August 27, 2002Data Mining and Text-based Information - Mark Wasson 42 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? What is the forecast for KDT?

43 August 27, 2002Data Mining and Text-based Information - Mark Wasson 43 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 KDT,

44 August 27, 2002Data Mining and Text-based Information - Mark Wasson 44 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 KDT, 2000+

45 August 27, 2002Data Mining and Text-based Information - Mark Wasson 45 KDT is emerging, but slowly Still in early stages Lots of promise The Forecast

46 August 27, 2002Data Mining and Text-based Information - Mark Wasson 46 Information Sources and Links

47 August 27, 2002Data Mining and Text-based Information - Mark Wasson 47 KDnuggets, ACM Special Interest Group in Knowledge Discovery and Data Mining, Association for Computational Linguistics, Data Mining and Knowledge Discovery (journal), Kluwer Academic Publishers, Companies, Glossary of Terms, Resources

48 August 27, 2002Data Mining and Text-based Information - Mark Wasson 48 The 3 rd SIAM International Conference on Data Mining, May 1-3, 2003, San Francisco, CA North American Association for Computational Linguistics/Human Language Technology Joint Conference, approx. early June, 2003, Edmonton, AB The 9 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC Related Technical Conferences

49 August 27, 2002Data Mining and Text-based Information - Mark Wasson 49 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. Books

50 August 27, 2002Data Mining and Text-based Information - Mark Wasson 50 Attensity, Alias I, Caesius, ClearForest, Columbia University, Cymfony, eNeuralNet, Hewlett Packard Labs, IBM, Company Links

51 August 27, 2002Data Mining and Text-based Information - Mark Wasson 51 Inforsense, Insightful, Inxight, John Benjamins Publishing, bin/t_bookview.cgi?bookid=NLP_5 Megaputer Intelligence, Nstein, SAS, SPSS, SRA International, Company Links

52 August 27, 2002Data Mining and Text-based Information - Mark Wasson 52 Stratify, University of Massachusetts-Amherst, University of Sheffield, Verity, Company Links

53 August 27, 2002Data Mining and Text-based Information - Mark Wasson 53 Adamic, L., Wilkinson, D., Huberman, B., & Adar, E. (2002). A Literature Based Method for Identifying Gene-Disease Connections. Proceedings of the 1 st IEEE Computer Society Bioinformatics Conference. Lavrenko, V., Schmill, M., Lawrie, D., Ogilvie, P., Jensen, D., & Allan, J. (2000). Language Models for Financial News Recommendation. Proceedings of the 9 th International Conference on Information and Knowledge Management. Lent, B., Agrawal, R., & Srikant, R. (1997). Discovering Trends in Text Databases. Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining. Shewhart, M., & Wasson, M. (1999). Monitoring Newsfeeds for Hot Topics. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Swanson, D., & Smalheiser, N. (1996). Undiscovered Public Knowledge: A Ten-year Update. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Data Mining/Text References

54 August 27, 2002Data Mining and Text-based Information - Mark Wasson 54 Questions? You can also contact me at


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