1. Text Mining: Finding Nuggets in Mountains of Textual Data Jochen Dijrre, Peter Gerstl, Roland Seiffert Presented by Drew DeHaas

2. Outline  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  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. Motivation  Rapid processing of large document collections  Speed!  Automation of tasks  Objective analysis

5. Typical Applications  Summarizing documents  Discovering/monitoring relations among people, places, organizations, etc  Organizing documents by content  Indexing for search and retrieval  Retrieving documents by content

6. Outline  Motivation  Methodology  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. Comparison with Data Mining  Data mining Identify data set(s) Select features manually Prepare data Analyze distribution  Text mining Identify documents Extract features Select features (automatically) Prepare data Analyze distribution

11. IBM Intelligent Miner for Text  IBM introduced product in 1998  SDK with: Feature extraction, clustering, categorization, and more  Traditional components (search engine, etc)  No longer available?  The rest of the paper describes text mining methodology of Intelligent Miner.

12. Outline  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Conclusion & Exam Questions

13. 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

14. 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

15. 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

16. Feature Extraction Example  Disambiguating Proper Names (Nominator Program) Apply heuristics to strings, instead of interpreting semantics The unit of context for extraction is a document. The heuristics represent English naming conventions

17. 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

18. Outline  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Conclusion & Exam Questions

19. 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

20. 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

21. Clustering Model

22. 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

23. Categorization Model

24. Outline  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Conclusion & Exam Questions

25. 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”

26. 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

27. 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

28. Outline  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Conclusion & Exam Questions

29. 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

30. 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

31. 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

32. 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.

33. Questions?