1. Jochen Dijrre, Peter Gerstl, Roland Seiffert Presented by Shamil Mustafayev 04/16/2013 1

2. Outline  Definition  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Comparison with Data Mining  Conclusion & Exam Questions 2

3. Definition  Text Mining: the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. 3

4. Outline  Definition  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Comparison with Data Mining  Conclusion & Exam Questions 4

5. Motivation  A large portion of a company’s data is unstructured or semi-structured 5  Letters  Emails  Phone transcripts  Contracts  Technical documents  Patents  Web pages  Articles

6. 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

7. Outline  Definition  Motivation  Methodology  Comparison with Data Mining  Feature Extraction  Clustering and Categorizing  Some Applications  Conclusion & Exam Questions 7

8. Methodology: Challenges  Information is in unstructured textual form  Natural language interpretation is difficult & complex task! (not fully possible) Google and Watson are a step closer  Text mining deals with huge collections of documents Impossible for human examination 8

9. Google vs Watson  Google justifies the answer by returning the text documents where it found the evidence.  Google finds documents that are most suitable to a given Keyword. 9  Watson tries to understand the semantics behind a given key phrase or question.  Then Watson will use its huge knowledge base to find the correct answer.  Watson uses more AI

10. Methodology: Two Aspects  Knowledge Discovery Extraction of codified information ○ Feature Extraction Mining proper; determining some structure  Information Distillation Analysis of feature distribution 10

11. 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 11

12. Outline  Definition  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Comparison with Data Mining  Conclusion & Exam Questions 12

13. Feature Extraction  Recognize and classify “significant” vocabulary items from the text  Categories of vocabulary Proper names – Mrs. Albright or Dheli[sic], India Multiword terms – Joint venture, online document Abbreviations – CPU, CEO Relations – Jack Smith-age-42 Other useful things: numerical forms of numbers, percentages, money, etc 13

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 14

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 15

16. 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. 16

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 17

18. Outline  Definition  Motivation  Methodology  Comparison with Data Mining  Feature Extraction  Clustering and Categorizing  Some Applications  Conclusion & Exam Questions 18

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 19

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 20

21. Clustering Model 21

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 22

23. Categorization Model 23

24. Outline  Definition  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Comparison with Data Mining  Conclusion & Exam Questions 24

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” 25

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 26

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 27

28. Outline  Definition  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Comparison with Data Mining  Conclusion & Exam Questions 28

29. Comparison with Data Mining  Data mining Discover hidden models. tries to generalize all of the data into a single model. marketing, medicine, health care 29  Text mining Discover hidden facts. tries to understand the details, cross reference between individual instances biosciences, customer profile analysis

30. Outline  Definition  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Some Applications  Comparison with Data Mining  Conclusion & Exam Questions 30

31. 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 31

32. Exam Question #1  What are the two aspects 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 32

33. 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 33

34. 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. 34

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