Text Based Information Retrieval - Text Mining

1. Text Based Information Retrieval - Text Mining PKB - Antonie

2. Background • Human dificults to process huge information • Computer can do better with matemathics • why don’t also use computer to process huge information? • A Large text to find: • Terrorist attack on 1995? • Terrorist movement and bomb relation? • Relates to Information Retreival, Data Mining and Text Mining

3. Terminology • Data Mining A step in the knowledge discovery process consisting of particular algorithms (methods), produces a particular enumeration of patterns (models) over the data. • Data Mining is a process of discovering advantageous patterns in data. • Knowledge Discovery Process The process of using data mining methods (algorithms) to extract (identify) what is knowledge according to the specifications of measures and thresholds, using a database along with any necessary preprocessing or transformations.

4. What kind of data in Data Mining? • Data Mining Application: • Market analysis • Risk analysis and management • Fraud detection and detection of unusual patterns (outliers) • Text mining (news group, email, documents) and Web mining • Stream data mining • Relational Databases • Data Warehouses • Transactional Databases • Advanced Database Systems • Object-Relational • Multimedia • Text • Heterogeneous and Distributed • WWW

5. Knowledge Discovery

6. Required effort for each KDD Step • Arrows indicate the direction we hope the effort should go.

7. Text Mining

8. What Is Text Mining? “The objective of Text Mining is to exploit information contained in textual documents in various ways, including …discovery of patterns and trends in data, associations among entities, predictive rules, etc.” (Grobelnik et al., 2001) “Another way to view text data mining is as a process of exploratory data analysis that leads to heretofore unknown information, or to answers for questions for which the answer is not currently known.” (Hearst, 1999) “The non trivial extraction of implicit, previously unknown, and potentially useful information from (large amount of) textual data”. An exploration and analysis of textual (natural-language) data by automatic and semi automatic means to discover new knowledge.

9. Text Mining (2) • What is “previously unknown”information ? • Strict definition • Information that not even the writer knows. • Lenient (lunak) definition • Rediscover the information that the author encoded in the text • e.g., Automatically extracting a product’s name from a web-page.

10. Text Mining Methods • Information Retrieval • Indexing and retrieval of textual documents • Information Extraction • Extraction of partial knowledge in the text • Web Mining • Indexing and retrieval of textual documents and extraction of partial knowledge using the web • Clustering • Generating collections of similar text documents

11. Text Mining Application • Email: Spam filtering • News Feeds: Discover what is interesting • Medical: Identify relationships and link information from different medical fields • Marketing: Discover distinct groups of potential buyers and make suggestions for other products • Industry: Identifying groups of competitors web pages • Job Seeking: Identify parameters in searching for jobs

12. TM - Information Retrieval

13. Information Retrieval (1) • Given: • A source of textual documents • A well defined limited query (text based) • Find: • Sentences with relevant information • Extract the relevant information and ignore non-relevant information (important!) • Link related information and output in a predetermined format • Example: news stories, e-mails, web pages, photograph, music, statistical data, biomedical data, etc. • Information items can be in the form of text, image, video, audio, numbers, etc.

14. Information Retrieval (2) • 2 basic information retrieval (IR) process: • Browsing or navigation system • User skims document collection by jumping from one document to the other via hypertext or hypermedia links until relevant document found • Classical IR system: question answering system • Query: question in natural language • Answer: directly extracted from text of document collection • Text Based Information Retrieval: • Information item (document) : • Text format (written/spoken) or has textual description • Information need (query): • Usually in text format

15. Classical IR System Process

17. Intelligent Information Retrieval • meaning of words • Synonyms “buy” / “purchase” • Ambiguity “bat” (baseball vs. mammal) • order of words in the query • hot dog stand in the amusement park • hot amusement stand in the dog park

18. TM - Web Mining

19. Why Mine the Web? • Enormous wealth of textual information on the Web. • Book/CD/Video stores (e.g., Amazon) • Restaurant information (e.g., Zagats) • Car prices (e.g., Carpoint) • Lots of data on user access patterns • Web logs contain sequence of URLs accessed by users • Possible to retrieve “previously unknown” information • People who ski also frequently break their leg. • Restaurants that serve sea food in California are likely to be outside San-Francisco

20. Web 1. Doc1 2. Doc2 3. Doc3 . . Mining the Web Documents source Spider IR / IE System Query Ranked Documents

21. Find: • Several clusters of documents that are relevant to each other Doc Doc Doc Doc Doc Doc Doc Doc Doc Doc What is Web Clustering ? Documents source • Given: • A source of textual documents • Similarity measure • e.g., how many words are common in these documents Similarity measure Clustering System

22. Text characteristics • Large textual data base • Efficiency consideration • over 2,000,000,000 web pages • almost all publications are also in electronic form • High dimensionality (Sparse input) • Consider each word/phrase as a dimension • Dependency • relevant information is a complex conjunction of words/phrases • e.g., Document categorization.Pronoun disambiguation

23. Text characteristics • Ambiguity • Word ambiguity • Pronouns (he, she …) • “buy”, “purchase” • Semantic ambiguity • The king saw the rabbit with his glasses. (? meanings) • Noisy data • Example: Spelling mistakes • Not well structured text • Chat rooms • “r u available ?” • “Hey whazzzzzz up” • Speech

24. Text mining process • Text preprocessing • Syntactic/Semantic text analysis • Features Generation • Bag of words • Features Selection • Simple counting • Statistics • Text/Data Mining • Classification- Supervised learning • Clustering- Unsupervised learning • Analyzing results

25. Syntactic / Semantic text analysis • Part Of Speech (pos) tagging • Find the corresponding pos for each word e.g., John (noun) gave (verb) the (det) ball (noun) • Word sense disambiguation • Context based or proximity based • Very accurate • Parsing • Generates a parse tree (graph) for each sentence • Each sentence is a stand alone graph

26. Feature Generation: Bag of words • Text document is represented by the words it contains (and their occurrences) • e.g., “Lord of the rings”  {“the”, “Lord”, “rings”, “of”} • Highly efficient • Makes learning far simpler and easier • Order of words is not that important for certain applications • Stemming: identifies a word by its root • Reduce dimensionality • e.g., flying, flew  fly • Use Porter Algorithm • Stop words: The most common words are unlikely to help text mining • e.g., “the”, “a”, “an”, “you” …

27. Feature selection • Reduce dimensionality • Learners have difficulty addressing tasks with high dimensionality • Irrelevant features • Not all features help! • e.g., the existence of a noun in a news article is unlikely to help classify it as “politics” or “sport” • Use Weightening

28. Text Mining: Classification definition • Given: a collection of labeled records (training set) • Each record contains a set of features (attributes), and the true class (label) • Find: a model for the class as a function of the values of the features • Goal: previously unseen records should be assigned a class as accurately as possible • A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it

29. Similarity Measures: • Euclidean Distance if attributes are continuous • Other Problem-specific Measures • e.g., how many words are common in these documents Text Mining: Clustering definition • Given: a set of documents and a similarity measure among documents • Find: clusters such that: • Documents in one cluster are more similar to one another • Documents in separate clusters are less similar to one another • Goal: • Finding a correct set of documents

30. Supervised vs. Unsupervised Learning • Supervised learning (classification) • Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations • New data is classified based on the training set • Unsupervised learning (clustering) • The class labels of training data is unknown • Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data

31. Evaluation:What Is Good Classification? • Correct classification: The known label of test sample is identical with the class result from the classification model • Accuracy ratio: the percentage of test set samples that are correctly classified by the model • A distance measure between classes can be used • e.g., classifying “football” document as a “basketball” document is not as bad as classifying it as “crime”.

32. Evaluation: What Is Good Clustering? • Good clustering method: produce high quality clusters with . . . • high intra-class similarity • low inter-class similarity • The qualityof a clustering method is also measured by its ability to discover some or all of the hidden patterns

33. Test Set Model Text Classification: An Example text class Learn Classifier Training Set

34. class text Splitting Attributes English No Yes MarSt Married Single, Divorced Income NO > 80K < 80K YES NO The splitting attribute at a node is determined based on a specific Attribute selection algorithm Decision Tree: A Text Example Yes

35. Classification by DT Induction • Decision tree • A flow-chart-like tree structure • Internal node denotes a test on an attribute • Branch represents an outcome of the test • Leaf nodes represent class labels or class distribution • Decision tree generation consists of two phases: • Tree construction • Tree pruning • Identify and remove branches that reflect noise or outliers • Use of decision tree: Classifying an unknown sample • Test the attribute of the sample against the decision tree

36. Summary • Text is tricky to process, but “ok” results are easily achieved • There exist several text mining systems • e.g., D2K - Data to Knowledge • http://www.ncsa.uiuc.edu/Divisions/DMV/ALG/ • Additional Intelligencecan be integrated with text mining • One may play with any phase of the text mining process

37. Summary • There are many other scientific and statistical text mining methods developed but not covered in this talk. • http://www.cs.utexas.edu/users/pebronia/text-mining/ • http://filebox.vt.edu/users/wfan/text_mining.html • Also, it is important to study theoretical foundations of data mining. • Data Mining Concepts and Techniques / J.Han & M.Kamber • Machine Learning, / T.Mitchell