Information Retrieval Models

1. Information Retrieval Models School of Informatics Dept. of Library and Information Studies Dr. Miguel E. Ruiz

2. What is Information Retrieval • Information Retrieval deals with information items in terms of • Representation • Storage • Organization • Access • An IR system should provide access to the information that the user is interested in.

3. Example of an Information need “Find all documents containing information on college tennis teams which: (1) are maintained by an university in the USA and (2) participate in the NCAA tennis tournament. To be relevant, the document must include information on the national ranking of the team in the last three years and the email or phone number of the team coach.”

4. The user must translate his/her information needs into a query. • Most commonly a query is a set of keywords that summarizes the information needs.

5. Information Retrieval vs. Data Retrieval • Data retrieval consists of determining which documents of the collection contain the keywords in the user query. • Information retrieval should “interpret” the contents of the documents in the collection and retrieve all the documents that are relevant to the user query while retrieving a few non relevant documents as possible.

6. Basic Concepts • Effective retrieval of relevant information is affected by: • the user task • the logical view of the documents

7. The User Task Retrieval Database Browsing

8. Logical View of a Document • Documents can be represented as: • A set of keywords or indexing terms • Full text

9. Logical View of the Document Text + Structure Document Automatic or manual indexing. Accents, Spacing, ect. Noun groups stopwords stemming Text Structure Recognition Index term Full text Structure

10. The Retrieval Process User Interface Text User’s need Text Text Operations Logical view Query Operations Indexing DB Manager Module User’s feedback Query Inverted file Index Searching Text Database Retrieved Docs Ranking Ranked Docs

11. IR Models • An IR model is a quadruple [D,Q,F, R(qi,dj)] • D: set of logical representations of the documents • Q: set of logical representation of the queries • F : framework for modeling document representations, queries, and their relationships • R(qi,dj): ranking function that defines an association between the query and the documents. This ranking defines an ordering among the documents regarding the query.

12. Set Theoretic • Fuzzy • Extended Boolean Classic Models • Boolean • Vector space • Probabilistic User Tasks • Retrieval: • Adhoc • Filtering Algebraic • Generalized Vector • Lat. Semantic Index • Neural Networks Structured Models • Non Overlapping • Lists • Proximal nodes Probabilistic Browsing • Inference Network • Belief Network Browsing • Flat • Structure Guided • Hypertext

13. Index Terms Full Text Full Text + Structure Retrieval Classic Set Theoretic Algebraic Probabilistic Classic Set Theoretic Algebraic Probabilistic Structured Browsing Flat Flat Hypertext Structured Guided Hypertext Retrieval Models and Logical View of Documents

14. IR Models • Basic concepts: • Each document is described as a set of representative keywords called index terms. • An index term is a word (which can be in the document) that helps in remembering the document’s main themes. • Index terms are used to index and summarize the document contents

15. IR Models • Basic concepts (cont.) • Index terms have varying relevance when used to describe the document contents. This effect is captured by assigning numerical weights to each index term in the document. • A weight is a positive value associated with each index term in the document.

16. IR Models • The Boolean Model is a simple retrieval model based on set theory and Boolean algebra. • Documents are represented by the index terms assigned to the document. There is no indication on which terms are more important than others ( weights are binary either 0 or 1)

17. IR Models • Boolean Model (cont.) • The Boolean operators used are • Conjunction (AND, ) • Disjunction (OR, ) • Negation (NOT, ) • Queries are specified as conventional Boolean expressions which can be represented as a disjunction of conjunctive forms vectors (disjunctive normal form - DNF)

18. IR Models • Boolean model Examples: Q = Safety  (Car  Industry) Qdnf= (1,1,1)  (1,1,0)  (1,0,0)

19. IR Models • Boolean Model

20. IR Models • Vector Space Model • Documents and queries are expressed using a vector whose components are all the possible index terms(t). Each index term has an associated weight that indicates the importance of the index term in the document (or query).

21. IR Models • In other words, the document dj and the query q are represented as t-dimensional vectors. dj  q

22. IR Model • The vector space model proposes to evaluate the degree of similarity of document dj with regard to the query q as the correlation between the two vectors dj and q.

23. IR Models • This correlation can be quantified in different way, for example by the cosine of the angle between these two vectors.

24. IR Models • Since wi,j 0 and Wi,q 0 , sim(dj,q) varies between 0 to +1. The vector space model assumes that the similarity value is an indication of the relevance of the document to the given query. Thus space models ranks the retrieved documents by the similarity value.

25. IR Models • How can we compute the values of the weights wi,j? • One of the most popular methods is based on combining two factors: • The importance of each index term in the document • The importance of the index term in the collection

26. IR Models • Importance of the index term in the document: • This can be measured by the number of times that the term appears in the document. The higher the number of times that it is mentioned in the document the better the term is. This is called the term frequency which is denoted by the symbol tf.

27. IR Models • The importance of the index term in the collection: • An index term that appears in every document in the collection is not very useful, but a term that occurs in only a few documents may indicate that these few documents could be relevant to a query that uses this term.

28. IR Model In other words, the importance of an index term in the collection is quantified by the inverse of the frequency that this term has among the documents in the collection. This factor is usually called the inverse document frequency or the idf factor.

29. IR Models Mathematically this can be expressed as: Where: N= number of documents in the collection ni = number of document that contain the term i

30. IR Models • Combining these two factors we can obtain the weight of an index term i as: Also called the tf-idf weighting scheme

31. IR Models • Vector Space Model

32. IR Models • Probabilistic model • Assumption: given a query q and a document dj in the collection, this model tries to estimate the probability that the user will find the document dj relevant. • The model assumes that there exists an ideal subset R of the document collection that contains only relevant documents.

33. IR Models • Similarity in the probabilistic model is computed as: Where: P(ki|R) is the probability that index term ki is present in a document randomly selected from the set R

34. IR Models _ • Estimation of P(ki|R) and P(ki|R): • Initial constant values: • Iterative process to improve estimates V is the set of documents retrieved Vi is the set of documents in V that contain ki

35. IR Models • Probabilistic Model

36. Set Theoretic • Fuzzy • Extended Boolean Classic Models • Boolean • Vector space • Probabilistic User Tasks • Retrieval: • Adhoc • Filtering Algebraic • Generalized Vector • Lat. Semantic Index • Neural Networks Structured Models • Non Overlapping • Lists • Proximal nodes Probabilistic Browsing • Inference Network • Belief Network Browsing • Flat • Structure Guided • Hypertext

37. Retrieval Evaluation Relevant docs in the Answer set |Ra| Collection Rel. docs |R| Answer set |A|

38. Retrieval Evaluation • Pooling System 1 System 2 System 3 … Use top K documents from each run pooling Pool of combined results User evaluation

39. Retrieval Evaluation • Relevance: • Strength: it involves people (users) as judges of effectiveness of performance. • Weakness: It involves judgments by people, with all the associated problems of subjectivity and variability.

40. Retrieval Evaluation • Types of relevance: (Saracevic, 1999) • System or algorithmic relevance: relation between the query and information objects retrieved. • Topical or Subject relevance: relation between the topic expressed in the query and the topic covered in the retrieved documents. • Cognitive relevance or pertinence: relation between the state of knowledge and cognitive information need of a user and documents retrieved. • Situational relevance or utility: relation between the task or problem at hand ant and the documents retrieved. • Motivational or affective relevance: relation between the intents, goals and motivations of a user and the documents retrieved.

41. Retrieval Evaluation • T. Joachims. Evaluating retrieval performance using clickthrough data. In Text Mining: theoretical aspects and applications. Physca-Verlag, 79-96, 2003. • Available online: http://www.cs.cornell.edu/People/tj/publications/joachims_02b.pdf

42. Clickthrough for IR Performance • Main idea: • Use a unified interface to submit results to two search engines. • Estimate relevance information from links visited from the results returned by a search engine.

43. Clickthrough for IR Performance • Regular clickthrough data: • User types a query into a unified interface. • The query is sent to both search engines A & B. • Randomly select one list of results and present it to the user • Ranks of links clicked by user are recorded

44. Clickthrough for IR Performance • Unbiased clickthrough data for comparing retrieval functions: • Blind test: Interface should hide the random variables underlying the hypothesis test to avoid biasing the user response. (placebo effect) • Click  Preference: design the interface so that interaction with the system demonstrates a particular judgement by the user. • Low usability impact

45. Clickthrough for IR Performance Input: ranking A = (a1, a2,…), ranking B= (b1, b2,…) Call: combine(A,B,0,0,) Output:combined ranking D Combine (A,B,ka,kb,D) if (ka = kb) { if(A[ka+1]  D) { D:= D+A[ka+1];} combine (A,B,ka+1,kb,D); } else { if (B[kb+1]  D) { D:= D+B[kb+1];} combine (A,B,ka,kb+1,D); }

46. Clickthrough for IR Performance • Hypothesis test: • Use a binomial sign test (i.e., McNemar’s test) to detect significant deviations from the median

47. Clickthrough for IR Performance • Experimental design: • Interface that sends query to two systems: • Google and MSNsearch. • Google and Default (50 links from MSNsearch in reverse orde) • MSNsearch and Default

48. Clickthrough for IR Performance • Does the clickthrough evaluation agree with the relevance judgements? • Their conclusion in the small experiment is that there is a string correlation between relevance judgements and clickthrough data. • Can this be a generalized conclusion?