Text Mining: Finding Nuggets in Mountains of Textual Data
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Text Mining: Finding Nuggets in Mountains of Textual Data

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Text Mining: Finding Nuggets in Mountains of Textual Data Presentation Transcript

  • 1. Text Mining: Finding Nuggets in Mountains of Textual Data Jochen Dijrre, Peter Gerstl, Roland Seiffert Presented by Huimin Ye
  • 2. Outline
    • Motivation
    • Methodology
    • Feature Extraction
    • Clustering and Categorizing
    • Some Applications
    • Comparison with Data Mining
    • Conclusion & Exam Questions
  • 3. Motivation
    • A large portion of a company’s data is unstructured or semi-structured
    • Letters
    • Emails
    • Phone recordings
    • Contracts
    • Technical documents
    • Patents
    • Web pages
    • Articles
  • 4. Definition
    • Text Mining:
      • the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources
  • 5. Typical Applications
    • Summarizing documents
    • Discovering/monitoring relations among people, places, organizations, etc
    • Customer profile analysis
    • Trend analysis
    • Documents summarization
    • Spam Identification
    • Public health early warning
    • Event tracks
  • 6. Outline
    • Motivation
    • Methodology
    • Comparison with Data Mining
    • Feature Extraction
    • Clustering and Categorizing
    • Some Applications
    • Conclusion & Exam Questions
  • 7. Methodology: Challenges
    • Information is in unstructured textual form
    • Natural language interpretation is difficult & complex task! (not fully possible)
    • Text mining deals with huge collections of documents
  • 8. Methodology: Two Aspects
    • Knowledge Discovery
      • Extraction of codified information
      • Mining proper; determining some structure
    • Information Distillation
      • Analysis of feature distribution
  • 9. Two Text Mining Approaches
    • Extraction
      • Extraction of codified information from single document
    • Analysis
      • Analysis of the features to detect patterns, trends, etc, over whole collections of documents
  • 10. Outline
    • Motivation
    • Methodology
    • Feature Extraction
    • Clustering and Categorizing
    • Some Applications
    • Comparison with Data Mining
    • Conclusion & Exam Questions
  • 11. IBM Intelligent Miner for Text
    • IBM introduced Intelligent Miner for Text in 1998
    • SDK with: Feature extraction, clustering, categorization, and more
    • Traditional components (search engine, etc)
    • The rest of the paper describes text mining methodology of Intelligent Miner.
  • 12. Feature Extraction
    • Recognize and classify “significant” vocabulary items from the text
    • Categories of vocabulary
      • Proper names
      • Multiword terms
      • Abbreviations
      • Relations
      • Other useful things: numerical forms of numbers, percentages, money, etc
  • 13. Canonical Form Examples
    • Normalize numbers, money
      • Four = 4, five-hundred dollar = $500
    • Conversion of date to normal form
    • Morphological variants
      • Drive, drove, driven = drive
    • Proper names and other forms
      • Mr. Johnson, Bob Johnson, The author = Bob Johnson
  • 14. Feature Extraction Approach
    • Linguistically motivated heuristics
    • Pattern matching
    • Limited lexical information (part-of-speech)
    • Avoid analyzing with too much depth
      • Does not use too much lexical information
      • No in-depth syntactic or semantic analysis
  • 15. Advantages to IBM’s approach
    • Processing is very fast (helps when dealing with huge amounts of data)
    • Heuristics work reasonably well
    • Generally applicable to any domain
  • 16. Outline
    • Motivation
    • Methodology
    • Comparison with Data Mining
    • Feature Extraction
    • Clustering and Categorizing
    • Some Applications
    • Conclusion & Exam Questions
  • 17. Clustering
    • Fully automatic process
    • Documents are grouped according to similarity of their feature vectors
    • Each cluster is labeled by a listing of the common terms/keywords
    • Good for getting an overview of a document collection
  • 18. Two Clustering Engines
    • Hierarchical clustering
      • Orders the clusters into a tree reflecting various levels of similarity
    • Binary relational clustering
      • Flat clustering
      • Relationships of different strengths between clusters, reflecting similarity
  • 19. Clustering Model
  • 20. Categorization
    • Assigns documents to preexisting categories
    • Classes of documents are defined by providing a set of sample documents.
    • Training phase produces “categorization schema”
    • Documents can be assigned to more than one category
    • If confidence is low, document is set aside for human intervention
  • 21. Categorization Model
  • 22. Outline
    • Motivation
    • Methodology
    • Feature Extraction
    • Clustering and Categorizing
    • Some Applications
    • Comparison with Data Mining
    • Conclusion & Exam Questions
  • 23. Applications
    • Customer Relationship Management application provided by IBM Intelligent Miner for Text called “Customer Relationship Intelligence”
      • “ Help companies better understand what their customers want and what they think about the company itself”
  • 24. Customer Intelligence Process
    • Take as input body of communications with customer
    • Cluster the documents to identify issues
    • Characterize the clusters to identify the conditions for problems
    • Assign new messages to appropriate clusters
  • 25. Customer Intelligence Usage
    • Knowledge Discovery
      • Clustering used to create a structure that can be interpreted
    • Information Distillation
      • Refinement and extension of clustering results
        • Interpreting the results
        • Tuning of the clustering process
        • Selecting meaningful clusters
  • 26. Outline
    • Motivation
    • Methodology
    • Feature Extraction
    • Clustering and Categorizing
    • Some Applications
    • Comparison with Data Mining
    • Conclusion & Exam Questions
  • 27. Comparison with Data Mining
    • Data mining
      • Discover hidden models.
      • tries to generalize all of the data into a single model.
      • marketing, medicine, health care
    • Text mining
      • Discover hidden facts.
      • tries to understand the details, cross reference between individual instances
      • biosciences, customer profile analysis
  • 28. Conclusion
    • This paper introduced text mining and how it differs from data mining proper.
    • Focused on the tasks of feature extraction and clustering/categorization
    • Presented an overview of the tools/methods of IBM’s Intelligent Miner for Text
  • 29. Exam Question #1
    • Name an example of each of the two main classes of applications of text mining.
      • Knowledge Discovery : Discovering a common customer complaint in a large collection of documents containing customer feedback.
      • Information Distillation : Filtering future comments into pre-defined categories
  • 30. Exam Question #2
    • How does the procedure for text mining differ from the procedure for data mining?
      • Adds feature extraction phase
      • Infeasible for humans to select features manually
      • The feature vectors are, in general, highly dimensional and sparse
  • 31. Exam Question #3
    • In the Nominator program of IBM’s Intelligent Miner for Text, an objective of the design is to enable rapid extraction of names from large amounts of text. How does this decision affect the ability of the program to interpret the semantics of text?
      • Does not perform in-depth syntactic or semantic analysis of the text; the results are fast but only heuristic with regards to actual semantics of the text.
  • 32. Questions?