data mining and text-based information mark wasson senior architect, research scientist lexisnexis
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Data Mining and Text-based Information Mark Wasson Senior Architect, Research Scientist LexisNexis [email protected] August 27, 2002. The Agenda. Knowledge Discovery, Data Mining, Text Mining From Free Text to Structured Metadata Knowledge Discovery and Data Mining in Text - PowerPoint PPT PresentationTRANSCRIPT
August 27, 2002 Data Mining and Text-based Information - Mark Wasson
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Data Mining and Text-based Information
Mark WassonSenior Architect, Research Scientist
LexisNexis
August 27, 2002
August 27, 2002 Data Mining and Text-based Information - Mark Wasson
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• 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
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Knowledge Discovery, Data Mining, Text Mining
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• 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?
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• 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
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• 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
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• 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
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• A synonym for Knowledge Discovery• The statistical/analytical processing within the
KDD process
What is Data Mining? (classic def’n)
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• 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 it’s new to you
What Isn’t Data Mining (classic def’n)
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• 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)
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• 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 def’n)
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• 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)
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• 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?
Today’s Key KDD Problems
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• 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
KDD and Text Problems
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Convert Information in Text to Metadata
How to “Fix” Text
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From Free Text to Structured Metadata
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• 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?
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• 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
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• 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
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• 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
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• Content-indicating annotations– Controlled vocabulary indexing– Statistically interesting extracted terms– Abstracts, summaries– Specialized fields– Domain templates
Content-based Metadata
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• 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
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• 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)
Metadata Creation Technologies
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• 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
Metadata Creation Technologies
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• 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
Metadata Creation Technologies
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• 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
Metadata Creation Technologies
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• 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
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• 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
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Knowledge Discovery and Data Mining in Text
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• 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
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• 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
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• Does document metadata have value for KDD applications in addition to its value for information finding and retrieval purposes?
• If so, where?
Research Problems
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• 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)
Example 1 – Trend Analysis
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• 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
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• 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
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• 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
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• 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
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• 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
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• 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
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• 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
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The Forecast for Data Mining and Text
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• 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?
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• 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, 1996-1999
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• 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+
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• KDT is emerging, but slowly
• Still in early stages
• Lots of promise
The Forecast
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Information Sources and Links
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• 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
Resources
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• 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/
Related Technical Conferences
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• 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
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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/
Company Links
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Inforsense, http://www.inforsense.com
Insightful, http://www.insightful.com
Inxight, http://www.inxight.com
John Benjamins Publishing, http://www.benjamins.com/cgi-bin/t_bookview.cgi?bookid=NLP_5
Megaputer Intelligence, http://www.megaputer.com
Nstein, http://www.nstein.com
SAS, http://www.sas.com
SPSS, http://www.spss.com
SRA International, http://www.sra.com
Company Links
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Stratify, http://www.stratify.com
University of Massachusetts-Amherst, http://ciir.cs.umass.edu/
University of Sheffield, http://gate.ac.uk/
Verity, http://www.verity.com
Company Links
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Adamic, L., Wilkinson, D., Huberman, B., & Adar, E. (2002). A Literature Based Method for Identifying Gene-Disease Connections. Proceedings of the 1st 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 9th 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
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