Special Topics on Information Retrieval

1. SpecialTopicsonInformationRetrieval Manuel Montes y Gómezhttp://ccc.inaoep.mx/~mmontesg/ mmontesg@inaoep.mx University of Alabama at Birmingham, Fall 2010.

2. Introduction

3. Content of thesection • Definition of the task • The vector space model • Performance evaluation • Main problems and basic solutions • Query expansion • Relevance feedback • Clustering (documents or results) Special Topics on Information Retrieval

4. Initial questions • What is an information retrieval system? • What is its goal? • What is inside it? Any sub-procceses? • How to evaluate its performance? • Why results are not always relevant? Special Topics on Information Retrieval

5. Firstdefinition “Information retrieval (IR) embraces the intellectual aspects of the description of information and its specification for search, and also whatever systems, techniques, or machines are employed to carry out the operation” Calvin Mooers, 1951 Special Topics on Information Retrieval

6. General scheme of the IR process Task Conception Info Need Formulation User Query Search Corpus Refinement Results Special Topics on Information Retrieval

7. More definitions • IR deals with the representation, storage, organization of, and access to information items. R. Baeza-Yatesand B. Ribeiro-Neto, 1999 • The task of an IR system is to retrieve documents or texts with information content that is relevant to a user’s information need. Spark Jones & Willett, 1997 Special Topics on Information Retrieval

8. Typical IR system Indexing Retrieval IR Model DocumentCollection Preprocessing Storing Index Querying Query Retrieving Results Special Topics on Information Retrieval

9. Vector space model • Documents are represented as vectors in a N-dimensional space • N is the number of terms in the collection • Term is different than word • Query is treated as any other document • Relevance – measured by similarity: • A document is relevant to the query if its vector is similar to the query’s vector . Special Topics on Information Retrieval

10. Preprocessing • Eliminate information about style, such as html or xml tags. • For some applications this information may be useful. For instance, only index some document sections. • Remove stop words • Functional words such as articles, prepositions, conjunctions are not useful (do not have an own meaning). • Perform stemmingorlemmatization • The goal is to reduce inflectional forms, and sometimes derivationally related forms. • am, are, is → be car, cars, car‘s → car Special Topics on Information Retrieval

11. Representation Whole vocabulary of the collection(all different terms) All documents (one vector per document) Weight indicating the contributionof term j in documenti. Special Topics on Information Retrieval

12. Term weighting - two main ideas • The importance of a term increases proportionally to the number of times it appears in the document. • It helps to describe document’s content. • The general importance of a term decreases proportionally to its occurrences in the entire collection. • Common terms are not good to distinguish relevant from non-relevant documents Special Topics on Information Retrieval

13. Term weighting – main approaches • Binary weights: • wi,j = 1 iff document di contains term tj , otherwise 0. • Term frequency (tf): • wi,j = (no. of occurrences of tj in di) • tf x idf weighting scheme: • wi,j = tf(tj, di) × idf(tj), where: • tf(tj, di) indicatestheocurrences of tj in documentdi • idf(tj) = log [N/df(tj)], wheredf(tj) isthenumber of documetsthatcontainthetermtj. Special Topics on Information Retrieval

14. Similarity measure • Relevance – similarity between document’s vectors and the query’s vector. • Measured by means of the cosene measure. • The closer the vectors (small their angle), the greater the document similarity. j d1 d2 a2 q a1 i Special Topics on Information Retrieval

15. Vector space model − Pros & Cons • Pros • Easily explain • Mathematically sound • Approximate query matching • Cons • Need term weighting • Hard to model structured queries • Normalization increases computational costs • Most commonly used IR model; it is considered superior to others due to its simplicity and elegancy. Special Topics on Information Retrieval

16. Other IR models • Boolean model (±1950) • Document similarity (±1957) • Probabilistic indexing (±1960) • Vector space model (±1970) • Probabilistic retrieval (±1976) • Fuzzy set models (±1980) • Inference networks (±1992) • Language models (±1998) Special Topics on Information Retrieval

17. IR evaluation • Why is evaluation important? • Which characteristics we need to evaluate? • How can we evaluate the performance of IR systems? • Given several systems, which one is the best? • What things (resources) are necessary to evaluate an IR system? • Is IR evaluation subjective or objective? Special Topics on Information Retrieval

18. Several perspectives • In order to answer "How well does the system work?“, we can investigated several options: • Processing: Time and space efficiency • Search: Effectiveness of results • System: Satisfaction of the user • We will focus on evaluating retrieval effectiveness, • How to measure the other aspects? Special Topics on Information Retrieval

19. Difficulties in evaluating an IR system • Effectiveness is related to the relevancy of retrieved items. • Relevancy is not typically binary but continuous. • Even if relevancy is binary, it can be a difficult judgment to make. • Relevancy, from a human standpoint, is: • Subjective: Depends upon a specific user’s judgment. • Situational: Relates to user’s current needs. • Cognitive: Depends on human perception and behavior. • Dynamic: Changes over time. Special Topics on Information Retrieval

20. Main requirements • It is necessary to have a test collection • A lot of documents (the bigger the better) • Several queries • Relevance judgments for all queries • Binary assessment of either relevant or non relevant for each query-document pair. • Methods/systems must be evaluated using the same evaluation measure. • Constructing a test collection requires considerable human effort Special Topics on Information Retrieval

21. Standard test collections • TREC (TextRetrievalConference) • National Institute of Standards and Technology • In total, 1.89 million documents and relevance judgments for 450 information needs • CLEF (Cross Language Evaluation Forum) • This evaluation series has concentrated on European languages and cross-language information retrieval • Last Adhoc English monolingual IR task: 169,477 documents and 50 queries. Special Topics on Information Retrieval

22. Retrieval effectiveness In response to a query, an IR system searches a document collection and returns a ordered list of responses. • Measure the quality of a set/list of responses • a better search strategy yields a better result list • Better result lists help the user fill their information need • Two kinds of measures: • set based and ranked-list based Special Topics on Information Retrieval

23. Set based measures Collection Query RelevantDocuments RelevantRetrieveddocuments RetrievedDocuments Results Special Topics on Information Retrieval

24. Precision, recall and F-measure • Precision (P) • The ability to retrievetop-ranked documents that are mostly relevant. • Recall (R) • The ability of the search to find all of the relevant items in the corpus. • F-measure (F) • Harmonic mean of recall and precision

25. Ranked-list based measures • Average Recall/Precision Curve • Plots average precision at each standard recall level across all queries. • MAP (mean average precision) • Provides a single-figure measure of quality across recall levels • R-prec • Precision at the R-th position in the ranking of results for a query that has R relevant documents Special Topics on Information Retrieval

26. Recall/Precision Curve(from Mooney’s IR course at the University of Texas at Austin) What is the curve of an ideal system? Special Topics on Information Retrieval

27. MAP • Average precision is the average of the precision scores at the rank locations of each relevant document. • Mean Average Precision is the mean of the Average Precision scores for a group of queries. N is the number of retrieved documents, P(i) is the precision of the first i documents, and rel(i) is a binary function indicating if document at i-position is relevant or not. Special Topics on Information Retrieval

28. Illustrative example(from IR course of Northeastern University, College of Computer and Information Science) MAP1 = 0.622 MAP2 = 0.52 Special Topics on Information Retrieval

29. Common problems • Why not all retrieved documents are relevant?, why it is too difficult to get 100% of precision? • Consider the query “jaguar” • Why it is complex to retrieve all relevant documents (get 100% of recall)? • Consider the query “religion” • What to do in order to tackle these problems? Special Topics on Information Retrieval

30. Query expansion • It is the process of adding terms to a user’s (weighted) query. • Its goal is to improve precision and/or recall. • Example: • User Query: “car” • Expanded Query: “car cars automobile automobiles auto” etc… • How to do it? Ideas? Special Topics on Information Retrieval

31. Main approaches • By means of a thesaurus • Thesauri may be manually or automatically constructed. • By means of (user) relevance feedback • Automatic query expansion • Local query expansion (blind feedback) • Global query expansion (using word associations) Special Topics on Information Retrieval

32. Thesaurus-based query expansion A thesaurus provides information on synonyms and semantically related words • Expansion procedure: For each term t in a query, expand the query with synonyms and related words of t. • Generally increases recall. • May significantly decrease precision, particularly with ambiguous terms. • “interest rate”  “interest rate fascinate evaluate” Special Topics on Information Retrieval

33. Relevance feedback • Basic procedure: • The user creates their initial query which returns an initial result set. • The user selects a list of documents that are relevant to their search. • The system then re-weights and/or expands the query based upon the terms in the documents • Significant improvement in recall and precision over early query expansion work Special Topics on Information Retrieval

34. Standard Rochio Method • The idea is to move the query in direction closer to the relevant documents, and farther away from the irrelevant ones. • : Tunable weight for initial query • : Tunable weight for relevant documents • : Tunable weight for irrelevant documents Special Topics on Information Retrieval

35. Pseudo relevance feedback Users do not like to givemanual feedback to the system • Use relevance feedback methods without explicit user input. • Just assume the top m retrieved documents are relevant, and use them to reformulate the query. • Relies largely on the systems ability to initially retrieve relevant documents. Special Topics on Information Retrieval

36. Automatic global analysis • Determine term similarity through a pre-computed statistical analysis of the complete corpus. • Compute association matrices which quantify term correlations in terms of how frequently they co-occur. • Expand queries with statistically most similar terms. • The same information for all queries. It is an offline process Special Topics on Information Retrieval

37. Clustering in information retrieval • Cluster hypothesis: Documents in the same cluster behave similarly with respect to relevance to information needs. • If there is a document from a cluster that is relevant to a search request, then it is likely that other documents from the same cluster are also relevant. • Two main uses: • Collectionclustering • Higherefficiency: fastersearch • Tendstoimprovesrecall • Searchresultsclustering • More effective information presentation to user Special Topics on Information Retrieval