Motivation and Outline

1. Motivation and Outline • Background • Definitions, etc. • The Problem • 100,000+ pages • The Solution • Ranking docs • Vector space • Extensions • Relevance feedback, • clustering, • query expansion, etc.

2. Motivation • IR: representation, storage, organization of, and access to information items • Focus is on the user information need • User information need: • Find all docs containing information on college tennis teams which: (1) are maintained by a USA university and (2) participate in the NCAA tournament. • Emphasis is on the retrieval of information (not data)

3. Motivation • Data retrieval • which docs contain a set of keywords? • Well defined semantics • a single erroneous object implies failure! • Information retrieval • information about a subject or topic • semantics is frequently loose • small errors are tolerated • IR system: • interpret contents of information items • generate a ranking which reflects relevance • notion of relevance is most important

4. Motivation • IR at the center of the stage • IR in the last 20 years: • classification and categorization • systems and languages • user interfaces and visualization • Still, area was seen as of narrow interest • Advent of the Web changed this perception once and for all • universal repository of knowledge • free (low cost) universal access • no central editorial board • many problems though: IR seen as key to finding the solutions!

5. Basic Concepts Retrieval Database Browsing • The User Task • Retrieval • information or data • purposeful • Browsing • glancing around • F1; cars, Le Mans, France, tourism

6. Text User Interface 4, 10 user need Text Text Operations (stemming, noun phrase detection etc..) 6, 7 logical view logical view Query Operations (elaboration, relevance feedback DB Manager Module Indexing user feedback 5 8 inverted file query Searching (hash tables etc.) Index 8 retrieved docs Text Database Ranking (vector models ..) ranked docs 2 The Retrieval Process

7. Measuring Performance tn • Precision • Proportion of selected items that are correct • Recall • Proportion of target items that were selected • Precision-Recall curve • Shows tradeoff fp tp fn System returned these Actual relevant docs Precision Recall

8. Precision/Recall Curves 11-point recall-precision curve Example: Suppose for a given query, 10 documents are relevant. Suppose when all documents are ranked in descending similarities, we have d1 d2d3 d4 d5d6 d7 d8 d9d10 d11d12d13 d14 d15 d16d17d18 d19d20 d21 d22 d23 d24d25d26 d27 d28d29 d30 d31 … precision 1.0 recall .1 .3

9. Precision Recall Curves… When evaluating the retrieval effectiveness of a text retrieval system or method, a large number of queries are used and their average 11-point recall-precision curve is plotted. • Methods 1 and 2 are better than method 3. • Method 1 is better than method 2 for high recalls. precision Method 1 Method 2 Method 3 recall

10. Query Models • IR systems usually adopt index terms to process queries • Index term: • a keyword or group of selected words • any word (more general) • Stemming might be used: • connect: connecting, connection, connections • An inverted file is built for the chosen index terms

11. Introduction Docs Index Terms doc match Ranking Information Need query

12. Introduction • Matching at index term level is quite imprecise • No surprise that users get frequently unsatisfied • Since most users have no training in query formation, problem is even worst • Frequent dissatisfaction of Web users • Issue of deciding relevance is critical for IR systems: ranking

13. Introduction • A ranking is an ordering of the documents retrieved that (hopefully) reflects the relevance of the documents to the user query • A ranking is based on fundamental premisses regarding the notion of relevance, such as: • common sets of index terms • sharing of weighted terms • likelihood of relevance • Each set of premisses leads to a distinct IR model

14. Algebraic Set Theoretic Generalized Vector Lat. Semantic Index Neural Networks Structured Models Fuzzy Extended Boolean Non-Overlapping Lists Proximal Nodes Classic Models Probabilistic boolean vector probabilistic Inference Network Belief Network Browsing Flat Structure Guided Hypertext IR Models U s e r T a s k Retrieval: Adhoc Filtering Browsing

15. Retrieval: Ad Hoc x Filtering • Ad hoc retrieval: Q1 Q2 Collection “Fixed Size” Q3 Q4 Q5

16. Retrieval: Ad Hoc x Filtering • Filtering: Docs Filtered for User 2 User 2 Profile User 1 Profile Docs for User 1 Documents Stream

17. Classic IR Models - Basic Concepts • Each document represented by a set of representative keywords or index terms • An index term is a document word useful for remembering the document main themes • Usually, index terms are nouns because nouns have meaning by themselves • However, search engines assume that all words are index terms (full text representation)

18. structure Full text Index terms Generating keywords • Logical view of the documents Accents spacing Noun groups Manual indexing Docs stopwords stemming structure • Stop-word elimination • Noun phrase detection • Stemming • Generating index terms • Improving quality of terms. • Synonyms, co-occurence detection, latent semantic indexing..

19. Classic IR Models - Basic Concepts • Not all terms are equally useful for representing the document contents: less frequent terms allow identifying a narrower set of documents • The importance of the index terms is represented by weights associated to them • Let • ki be an index term • dj be a document • wij is a weight associated with (ki,dj) • The weight wij quantifies the importance of the index term for describing the document contents

20. Classic IR Models - Basic Concepts • Ki is an index term • dj is a document • t is the total number of docs • K = (k1, k2, …, kt) is the set of all index terms • wij >= 0 is a weight associated with (ki,dj) • wij = 0 indicates that term does not belong to doc • vec(dj) = (w1j, w2j, …, wtj) is a weighted vector associated with the document dj • gi(vec(dj)) = wij is a function which returns the weight associated with pair (ki,dj)

21. The Boolean Model • Simple model based on set theory • Queries specified as boolean expressions • precise semantics • neat formalism • q = ka  (kb  kc) • Terms are either present or absent. Thus, wij  {0,1} • Consider • q = ka  (kb  kc) • vec(qdnf) = (1,1,1)  (1,1,0)  (1,0,0) • vec(qcc) = (1,1,0) is a conjunctive component

22. Ka Kb (1,1,0) (1,0,0) (1,1,1) Kc The Boolean Model • q = ka  (kb  kc) • sim(q,dj) = 1 if  vec(qcc) | (vec(qcc)  vec(qdnf))  (ki, gi(vec(dj)) = gi(vec(qcc))) 0 otherwise

23. Drawbacks of the Boolean Model • Retrieval based on binary decision criteria with no notion of partial matching • No ranking of the documents is provided (absence of a grading scale) • Information need has to be translated into a Boolean expression which most users find awkward • The Boolean queries formulated by the users are most often too simplistic • As a consequence, the Boolean model frequently returns either too few or too many documents in response to a user query

24. The Vector Model • Use of binary weights is too limiting • Non-binary weights provide consideration for partial matches • These term weights are used to compute a degree of similarity between a query and each document • Ranked set of documents provides for better matching

25. The Vector Model • Define: • wij > 0 whenever ki  dj • wiq >= 0 associated with the pair (ki,q) • vec(dj) = (w1j, w2j, ..., wtj) vec(q) = (w1q, w2q, ..., wtq) • To each term ki is associated a unitary vector vec(i) • The unitary vectors vec(i) and vec(j) are assumed to be orthonormal (i.e., index terms are assumed to occur independently within the documents) • The t unitary vectors vec(i) form an orthonormal basis for a t-dimensional space • In this space, queries and documents are represented as weighted vectors

26. Document Vectors • Documents are represented as “bags of words” • Represented as vectors when used computationally • A vector is like an array of floating point • Has direction and magnitude • Each vector holds a place for every term in the collection • Therefore, most vectors are sparse

27. Vector Space Example a: System and human system engineering testing of EPS b: A survey of user opinion of computer system response time c: The EPS user interface management system d: Human machine interface for ABC computer applications e: Relation of user perceived response time to error measurement f: The generation of random, binary, ordered trees g: The intersection graph of paths in trees h: Graph minors IV: Widths of trees and well-quasi-ordering i: Graph minors: A survey

28. We Can Plot the Vectors Star Doc about movie stars Doc about astronomy Doc about mammal behavior Diet

29. Documents in 3D Space Illustration from Jurafsky & Martin

30. Similarity Function (1) The similarity or closeness of a document d = ( w1, …, wi, …, wn ) with respect to a query q = ( q1, …, qi, …, qn ) is computed using a similarity function. Many similarity functions exist. • Dot product function sim(q, d) = dot(q, d) = q1  w1 + … + qn wn Example: Suppose d = (0.2, 0, 0.3, 1) and q = (0.75, 0.75, 0, 1), then sim(q, d) = 0.15 + 0 + 0 + 1 = 1.15

31. Similarity Function (2) Observations of the dot product function. • Documents having more terms in common with a query tend to have higher similarities with the query. • For terms that appear in both q and d, those with higher weights contribute more to sim(q, d) than those with lower weights. • It favors long documents over short documents. • The computed similarities have no clear upper bound.

32. A normalized similarity metric j dj  q i • Sim(q,dj) = cos() = [vec(dj)  vec(q)] / |dj| * |q| = [ wij * wiq] / |dj| * |q| • Since wij > 0 and wiq > 0, 0 <= sim(q,dj) <=1 • A document is retrieved even if it matches the query terms only partially

33. Vector Space Example cont. interface user c b system a

34. Answering a Query UsingVector Space • Represent query as vector • Compute distances to all documents • Rank according to distance • Example • “computer system”

35. The Vector Model • Sim(q,dj) = [ wij * wiq] / |dj| * |q| • How to compute the weights wij and wiq ? • Simple keyword frequencies tend to favor common words • E.g. Query: The Computer Tomography • A good weight must take into account two effects: • quantification of intra-document contents (similarity) • tf factor, the term frequency within a document • quantification of inter-documents separation (dissi-milarity) • idf factor, the inverse document frequency • wij = tf(i,j) * idf(i)

36. The Vector Model • Let, • N be the total number of docs in the collection • ni be the number of docs which contain ki • freq(i,j) raw frequency of ki within dj • A normalized tf factor is given by • f(i,j) = freq(i,j) / max(freq(l,j)) • where the maximum is computed over all terms which occur within the document dj • The idf factor is computed as • idf(i) = log (N/ni) • the log is used to make the values of tf and idf comparable. It can also be interpreted as the amount of information associated with the term ki.

37. The Vector Model • The best term-weighting schemes use weights which are give by • wij = f(i,j) * log(N/ni) • the strategy is called a tf-idfweighting scheme • For the query term weights, a suggestion is • wiq = (0.5 + [0.5 * freq(i,q) / max(freq(l,q)]) * log(N/ni) • The vector model with tf-idf weights is a good ranking strategy with general collections • The vector model is usually as good as the known ranking alternatives. It is also simple and fast to compute.

38. The Vector Model • Advantages: • term-weighting improves quality of the answer set • partial matching allows retrieval of docs that approximate the query conditions • cosine ranking formula sorts documents according to degree of similarity to the query • Disadvantages: • assumes independence of index terms (??); not clear that this is bad though