1. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 1 龙星计划课程 : 信息检索 Statistical Language Models for IR ChengXiang Zhai ( 翟成祥 ) Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology, Statistics University of Illinois, Urbana-Champaign http://www-faculty.cs.uiuc.edu/~czhai, czhai@cs.uiuc.edu

2. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 2 Outline More about statistical language models in general Systematic review of language models for IR –The basic language modeling approach –Advanced language models –KL-divergence retrieval model and feedback –Language models for special retrieval tasks

3. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 3 More about statistical language models in general

4. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 4 What is a Statistical LM? A probability distribution over word sequences –p(“ Today is Wednesday ”)  0.001 –p(“ Today Wednesday is ”)  0.0000000000001 –p(“ The eigenvalue is positive” )  0.00001 Context/topic dependent! Can also be regarded as a probabilistic mechanism for “generating” text, thus also called a “generative” model

5. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 5 Why is a LM Useful? Provides a principled way to quantify the uncertainties associated with natural language Allows us to answer questions like: –Given that we see “ John ” and “ feels ”, how likely will we see “ happy ” as opposed to “ habit ” as the next word? (speech recognition) –Given that we observe “baseball” three times and “game” once in a news article, how likely is it about “sports”? (text categorization, information retrieval) –Given that a user is interested in sports news, how likely would the user use “baseball” in a query? (information retrieval)

6. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 6 Source-Channel Framework (Model of Communication System [Shannon 48] ) Source Transmitter (encoder) Destination Receiver (decoder) Noisy Channel P(X) P(Y|X) X YX’ P(X|Y)=? When X is text, p(X) is a language model (Bayes Rule) Many Examples: Speech recognition: X=Word sequence Y=Speech signal Machine translation: X=English sentence Y=Chinese sentence OCR Error Correction: X=Correct word Y= Erroneous word Information Retrieval: X=Document Y=Query Summarization: X=Summary Y=Document

7. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 7 Basic Issues Define the probabilistic model –Event, Random Variables, Joint/Conditional Prob’s –P(w 1 w 2... w n )=f(  1,  2,…,  m ) Estimate model parameters –Tune the model to best fit the data and our prior knowledge –  i =? Apply the model to a particular task –Many applications

8. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 8 The Simplest Language Model (Unigram Model) Generate a piece of text by generating each word independently Thus, p(w 1 w 2... w n )=p(w 1 )p(w 2 )…p(w n ) Parameters: {p(w i )} p(w 1 )+…+p(w N )=1 (N is voc. size) Essentially a multinomial distribution over words A piece of text can be regarded as a sample drawn according to this word distribution

9. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 9 Text Generation with Unigram LM (Unigram) Language Model  p(w|  ) … text 0.2 mining 0.1 assocation 0.01 clustering 0.02 … food 0.00001 … Topic 1: Text mining … food 0.25 nutrition 0.1 healthy 0.05 diet 0.02 … Topic 2: Health Document d Text mining paper Food nutrition paper Sampling Given , p(d|  ) varies according to d

10. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 10 Estimation of Unigram LM (Unigram) Language Model  p(w|  )=? Document text 10 mining 5 association 3 database 3 algorithm 2 … query 1 efficient 1 … text ? mining ? assocation ? database ? … query ? … Estimation Total #words =100 10/100 5/100 3/100 1/100 How good is the estimated model ? It gives our document sample the highest prob, but it doesn’t generalize well… More about this later…

11. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 11 Empirical distribution of words There are stable language-independent patterns in how people use natural languages A few words occur very frequently; most occur rarely. E.g., in news articles, –Top 4 words: 10~15% word occurrences –Top 50 words: 35~40% word occurrences The most frequent word in one corpus may be rare in another

12. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 12 Zipf’s Law rank * frequency  constant Word Freq. Word Rank (by Freq) Most useful words (Luhn 57) Biggest data structure (stop words) Is “too rare” a problem? Generalized Zipf’s law:Applicable in many domains

13. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 13 More Sophisticated LMs N-gram language models –In general, p(w 1 w 2... w n )=p(w 1 )p(w 2 |w 1 )…p(w n |w 1 …w n-1 ) –n-gram: conditioned only on the past n-1 words –E.g., bigram: p(w 1... w n )=p(w 1 )p(w 2 |w 1 ) p(w 3 |w 2 ) …p(w n |w n-1 ) Remote-dependence language models (e.g., Maximum Entropy model) Structured language models (e.g., probabilistic context-free grammar) Will not be covered in detail in this course. If interested, read [Jelinek 98, Manning & Schutze 99, Rosenfeld 00]

14. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 14 Why Just Unigram Models? Difficulty in moving toward more complex models –They involve more parameters, so need more data to estimate (A doc is an extremely small sample) –They increase the computational complexity significantly, both in time and space Capturing word order or structure may not add so much value for “topical inference” But, using more sophisticated models can still be expected to improve performance...

15. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 15 Evaluation of SLMs Direct evaluation criterion: How well does the model fit the data to be modeled? –Example measures: Data likelihood, perplexity, cross entropy, Kullback-Leibler divergence (mostly equivalent) Indirect evaluation criterion: Does the model help improve the performance of the task? –Specific measure is task dependent –For retrieval, we look at whether a model helps improve retrieval accuracy –We hope more “reasonable” LMs would achieve better retrieval performance

16. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 16 What You Should Know How the source-channel framework can model many different problems Why unigram LMs seem to be sufficient for IR Zipf’s law

17. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 17 Systematic Review of Language Models for IR

18. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 18 Representative LMs for IR (up to 2006) 199819992000200120022003 Beyond unigram Song & Croft 99 Smoothing examined Zhai & Lafferty 01a Bayesian Query likelihood Zaragoza et al. 03. Theoretical justification Lafferty & Zhai 01a,01b Two-stage LMs Zhai & Lafferty 02 Lavrenko 04 Kraaij 04 Zhai 02 Dissertations Hiemstra 01 Berger 01 Ponte 98 Translation model Berger & Lafferty 99 Basic LM (Query Likelihood) URL prior Kraaij et al. 02 Lavrenko et al. 02 Ogilvie & Callan 03 Zhai et al. 03 Xu et al. 01 Zhang et al. 02 Cronen-Townsend et al. 02 Si et al. 02 Special IR tasks Xu & Croft 99 2004 Parsimonious LM Hiemstra et al. 04 Cluster smoothing Liu & Croft 04; Tao et al. 06 Relevance LM Lavrenko & Croft 01 Dependency LM Gao et al. 04 Model-based FB Zhai & Lafferty 01b Rel. Query FB Nallanati et al 03 Query likelihood scoring Ponte & Croft 98 Hiemstra & Kraaij 99; Miller et al. 99 Parameter sensitivity Ng 00 Title LM Jin et al. 02 Term-specific smoothing Hiemstra 02 Concept Likelihood Srikanth & Srihari 03 Time prior Li & Croft 03 Shen et al. 05 Srikanth 04 Kurland & Lee 05 Pesudo Query Kurland et al. 05 Rebust Est. Tao & Zhai 06 Thesauri Cao et al. 05 Query expansion Bai et al. 05 2005 - Markov-chain query model Lafferty & Zhai 01b Query/Rel Model & Feedback Cluster LM Kurland & Lee 04 Improved Basic LM Tan et al. 06 Tao 06 Kurland 06

19. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 19 Ponte & Croft’s Pioneering Work [Ponte & Croft 98] Contribution 1: –A new “query likelihood” scoring method: p(Q|D) –[Maron and Kuhns 60] had the idea of query likelihood, but didn’t work out how to estimate p(Q|D) Contribution 2: –Connecting LMs with text representation and weighting in IR –[Wong & Yao 89] had the idea of representing text with a multinomial distribution (relative frequency), but didn’t study the estimation problem Good performance is reported using the simple query likelihood method

20. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 20 Early Work (1998-1999) At about the same time as SIGIR 98, in TREC 7, two groups explored similar ideas independently: BBN [Miller et al., 99] & Univ. of Twente [Hiemstra & Kraaij 99] In TREC-8, Ng from MIT motivated the same query likelihood method in a different way [Ng 99] All following the simple query likelihood method; methods differ in the way the model is estimated and the event model for the query All show promising empirical results Main problems: –Feedback is explored heuristically –Lack of understanding why the method works….

21. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 21 Later Work (1999-) Attempt to understand why LMs work [Zhai & Lafferty 01a, Lafferty & Zhai 01a, Ponte 01, Greiff & Morgan 03, Sparck Jones et al. 03, Lavrenko 04] Further extend/improve the basic LMs [Song & Croft 99, Berger & Lafferty 99, Jin et al. 02, Nallapati & Allan 02, Hiemstra 02, Zaragoza et al. 03, Srikanth & Srihari 03, Nallapati et al 03, Li &Croft 03, Gao et al. 04, Liu & Croft 04, Kurland & Lee 04,Hiemstra et al. 04,Cao et al. 05, Tao et al. 06] Explore alternative ways of using LMs for retrieval (mostly query/relevance model estimation) [Xu & Croft 99, Lavrenko & Croft 01, Lafferty & Zhai 01a, Zhai & Lafferty 01b, Lavrenko 04, Kurland et al. 05, Bai et al. 05,Tao & Zhai 06] Explore the use of SLMs for special retrieval tasks [Xu & Croft 99, Xu et al. 01, Lavrenko et al. 02, Cronen-Townsend et al. 02, Zhang et al. 02, Ogilvie & Callan 03, Zhai et al. 03, Kurland & Lee 05, Shen et al. 05, Balog et al. 06, Fang & Zhai 07]

22. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 22 Review of LM for IR: Part 1. Basic Language Modeling Approach

23. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 23 The Basic LM Approach [Ponte & Croft 98] Document Text mining paper Food nutrition paper Language Model … text ? mining ? assocation ? clustering ? … food ? … food ? nutrition ? healthy ? diet ? … Query = “data mining algorithms” ? Which model would most likely have generated this query?

24. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 24 Ranking Docs by Query Likelihood d1d1 d2d2 dNdN q d1d1 d2d2 dNdN Doc LM p(q|  d 1 ) p(q|  d 2 ) p(q|  d N ) Query likelihood

25. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 25 Modeling Queries: Different Assumptions Multi-Bernoulli: Modeling word presence/absence –q= (x 1, …, x |V| ), x i =1 for presence of word w i ; x i =0 for absence –Parameters: {p(w i =1|d), p(w i =0|d)} p(w i =1|d)+ p(w i =0|d)=1 Multinomial (Unigram LM): Modeling word frequency –q=q 1,…q m, where q j is a query word –c(w i,q) is the count of word w i in query q –Parameters: {p(w i |d)} p(w 1 |d)+… p(w |v| |d) = 1 [Ponte & Croft 98] uses Multi-Bernoulli; most other work uses multinomial Multinomial seems to work better [Song & Croft 99, McCallum & Nigam 98,Lavrenko 04]

26. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 26 Retrieval as LM Estimation Document ranking based on query likelihood Retrieval problem  Estimation of p(w i |d) Smoothing is an important issue, and distinguishes different approaches Many smoothing methods are available Document language model

27. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 27 Which smoothing method is the best? It depends on the data and the task! Cross validation is generally used to choose the best method and/or set the smoothing parameters… For retrieval, Dirichlet prior performs well… Backoff smoothing [Katz 87] doesn’t work well due to a lack of 2 nd -stage smoothing… Note that many other smoothing methods exist See [Chen & Goodman 98] and other publications in speech recognition…

28. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 28 Comparison of Three Methods [Zhai & Lafferty 01a] Comparison is performed on a variety of test collections

29. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 29 The D ual-Role of Smoothing [Zhai & Lafferty 02] Verbose queries Keyword queries Why does query type affect smoothing sensitivity? long short long

30. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 30 Query = “the algorithms for data mining” Another Reason for Smoothing p( “algorithms”|d1) = p(“algorithm”|d2) p( “data”|d1) < p(“data”|d2) p( “mining”|d1) < p(“mining”|d2) So we should make p(“the”) and p(“for”) less different for all docs, and smoothing helps achieve this goal… Content words Intuitively, d2 should have a higher score, but p(q|d1)>p(q|d2)… p DML (w|d1): 0.04 0.001 0.02 0.002 0.003 p DML (w|d2): 0.02 0.001 0.01 0.003 0.004 Query = “the algorithms for data mining” P(w|REF) 0.2 0.00001 0.2 0.00001 0.00001 Smoothed p(w|d1): 0.184 0.000109 0.182 0.000209 0.000309 Smoothed p(w|d2): 0.182 0.000109 0.181 0.000309 0.000409

31. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 31 Two-stage Smoothing [Zhai & Lafferty 02] c(w,d) |d| P(w|d) = +  p(w|C) ++ Stage-1 -Explain unseen words -Dirichlet prior(Bayesian)  Collection LM (1- )+ p(w|U) Stage-2 -Explain noise in query -2-component mixture User background model Can be approximated by p(w|C)

32. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 32 Estimating  using leave-one-out [Zhai & Lafferty 02] P(w 1 |d - w 1 ) P(w 2 |d - w 2 ) log-likelihood Maximum Likelihood Estimator Newton’s Method Leave-one-out w1w1 w2w2 P(w n |d - w n ) wnwn...

33. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 33 Why would “leave-one-out” work? abc abc ab c d d abc cd d d abd ab ab ab ab cd d e cd e 20 word by author1 20 word by author2 abc abc ab c d d abe cb e f acf fb ef aff abef cdc db ge f s Suppose we keep sampling and get 10 more words. Which author is likely to “write” more new words? Now, suppose we leave “e” out…  must be big! more smoothing  doesn’t have to be big The amount of smoothing is closely related to the underlying vocabulary size

34. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 34 Estimating using Mixture Model [Zhai & Lafferty 02] Query Q=q 1 …q m 11 NN... Maximum Likelihood Estimator Expectation-Maximization (EM) algorithm P(w|d 1 )d1d1  P(w|d N )dNdN  …... Stage-1 (1- )p(w|d 1 )+ p(w|U) (1- )p(w|d N )+ p(w|U) Stage-2 Estimated in stage-1

35. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 35 Automatic 2-stage results  Optimal 1-stage results [Zhai & Lafferty 02] Average precision (3 DB’s + 4 query types, 150 topics) * Indicates significant difference Completely automatic tuning of parameters IS POSSIBLE!

36. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 36 Variants of the Basic LM Approach Different smoothing strategies –Hidden Markov Models (essentially linear interpolation) [Miller et al. 99] –Smoothing with an IDF-like reference model [Hiemstra & Kraaij 99] –Performance tends to be similar to the basic LM approach –Many other possibilities for smoothing [Chen & Goodman 98] Different priors –Link information as prior leads to significant improvement of Web entry page retrieval performance [Kraaij et al. 02] –Time as prior [Li & Croft 03] –PageRank as prior [Kurland & Lee 05] Passage retrieval [Liu & Croft 02]

37. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 37 Review of LM for IR: Part 2. Advanced Language Modeling Approaches

38. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 38 Improving the Basic LM Approach Capturing limited dependencies –Bigrams/Trigrams [Song & Croft 99]; Grammatical dependency [Nallapati & Allan 02, Srikanth & Srihari 03, Gao et al. 04] –Generally insignificant improvement as compared with other extensions such as feedback Full Bayesian query likelihood [Zaragoza et al. 03] –Performance similar to the basic LM approach Translation model for p(Q|D,R) [Berger & Lafferty 99, Jin et al. 02,Cao et al. 05] –Address polesemy and synonyms; improves over the basic LM methods, but computationally expensive Cluster-based smoothing/scoring [Liu & Croft 04, Kurland & Lee 04,Tao et al. 06] –Improves over the basic LM, but computationally expensive Parsimonious LMs [Hiemstra et al. 04]: –Using a mixture model to “factor out” non-discriminative words

39. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 39 Translation Models Directly modeling the “translation” relationship between words in the query and words in a doc When relevance judgments are available, (q,d) serves as data to train the translation model Without relevance judgments, we can use synthetic data [Berger & Lafferty 99], [Jin et al. 02], or thesauri [Cao et al. 05] Basic translation model Translation modelRegular doc LM

40. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 40 Cluster-based Smoothing/Scoring Cluster-based smoothing: Smooth a document LM with a cluster of similar documents [Liu & Croft 04] : improves over the basic LM, but insignificantly Document expansion smoothing: Smooth a document LM with the neighboring documents (essentially one cluster per document) [Tao et al. 06] : improves over the basic LM more significantly Cluster-based query likelihood: Similar to the translation model, but “translate” the whole document to the query through a set of clusters [Kurland & Lee 04] How likely doc D belongs to cluster C Only effective when interpolated with the basic LM scores Likelihood of Q given C

41. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 41 Feedback and Doc/Query Generation Classic Prob. Model Query likelihood (“Language Model”) Rel. doc model NonRel. doc model “Rel. query” model P(D|Q,R=1) P(D|Q,R=0) P(Q|D,R=1) (q 1,d 1,1) (q 1,d 2,1) (q 1,d 3,1) (q 1,d 4,0) (q 1,d 5,0) (q 3,d 1,1) (q 4,d 1,1) (q 5,d 1,1) (q 6,d 2,1) (q 6,d 3,0) Parameter Estimation Initial retrieval: - query as rel doc vs. doc as rel query - P(Q|D,R=1) is more accurate Feedback: - P(D|Q,R=1) can be improved for the current query and future doc - P(Q|D,R=1) can also be improved, but for current doc and future query Doc-based feedback Query-based feedback

42. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 42 Overview of Feedback Techniques Feedback as machine learning: many possibilities –Standard ML: Given examples of relevant (and non-relevant) documents, learn how to classify a new document as either “relevant” or “non-relevant”. –“Modified” ML: Given a query and examples of relevant (and non-relevant) documents, learn how to rank new documents based on relevance –Challenges: Sparse data Censored sample How to deal with query? –Modeling noise in pseudo feedback (as semi-supervised learning) Feedback as query expansion: traditional IR –Step 1: Term selection –Step 2: Query expansion –Step 3: Query term re-weighting Traditional IR is still robust (Rocchio), but ML approaches can potentially be more accurate

43. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 43 Difficulty in Feedback with Query Likelihood Traditional query expansion [Ponte 98, Miller et al. 99, Ng 99] –Improvement is reported, but there is a conceptual inconsistency –What’s an expanded query, a piece of text or a set of terms? Avoid expansion –Query term reweighting [Hiemstra 01, Hiemstra 02] –Translation models [Berger & Lafferty 99, Jin et al. 02] –Only achieving limited feedback Doing relevant query expansion instead [Nallapati et al 03] The difficulty is due to the lack of a query/relevance model The difficulty can be overcome with alternative ways of using LMs for retrieval (e.g., relevance model [Lavrenko & Croft 01], Query model estimation [Lafferty & Zhai 01b; Zhai & Lafferty 01b] )

44. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 44 Two Alternative Ways of Using LMs Classic Probabilistic Model :Doc-Generation as opposed to Query-generation –Natural for relevance feedback –Challenge: Estimate p(D|Q,R=1) without relevance feedback; relevance model [Lavrenko & Croft 01] provides a good solution Probabilistic Distance Model :Similar to the vector-space model, but with LMs as opposed to TF-IDF weight vectors –A popular distance function: Kullback-Leibler (KL) divergence, covering query likelihood as a special case –Retrieval is now to estimate query & doc models and feedback is treated as query LM updating [Lafferty & Zhai 01b; Zhai & Lafferty 01b] Both methods outperform the basic LM significantly

45. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 45 Relevance Model Estimation [Lavrenko & Croft 01] Question: How to estimate P(D|Q,R) (or p(w|Q,R)) without relevant documents? Key idea: –Treat query as observations about p(w|Q,R) –Approximate the model space with document models Two methods for decomposing p(w,Q) –Independent sampling (Bayesian model averaging) – Conditional sampling: p(w,Q)=p(w)p(Q|w) Original formula in [Lavranko &Croft 01]

46. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 46 Query Model Estimation [Lafferty & Zhai 01b, Zhai & Lafferty 01b] Question: How to estimate a better query model than the ML estimate based on the original query? “Massive feedback”: Improve a query model through co- occurrence pattern learned from –A document-term Markov chain that outputs the query [Lafferty & Zhai 01b] –Thesauri, corpus [Bai et al. 05,Collins-Thompson & Callan 05] Model-based feedback: Improve the estimate of query model by exploiting pseudo-relevance feedback –Update the query model by interpolating the original query model with a learned feedback model [ Zhai & Lafferty 01b] –Estimate a more integrated mixture model using pseudo- feedback documents [ Tao & Zhai 06]

47. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 47 Review of LM for IR: Part 3. KL-divergence retrieval model and feedback

48. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 48 Kullback-Leibler (KL) Divergence Retrieval Model Unigram similarity model Retrieval  Estimation of  Q and  D Special case: = empirical distribution of q recovers “query-likelihood” query entropy (ignored for ranking)

49. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 49 Feedback as Model Interpolation (Rocchio for Language Models) Query Q Document D Results Feedback Docs F={d 1, d 2, …, d n } Generative model  =0 No feedback  =1 Full feedback

50. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 50 Generative Mixture Model w w F={d 1, …, d n } Maximum Likelihood P(w|  ) P(w| C) 1- P(source) Background words Topic words = Noise in feedback documents

51. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 51 How to Estimate  F ? the 0.2 a 0.1 we 0.01 to 0.02 … text 0.0001 mining 0.00005 … Known Background p(w|C) … text =? mining =? association =? word =? … Unknown query topic p(w|  F )=? “Text mining” =0.7 =0.3 Observed Doc(s) Suppose, we know the identity of each word... ML Estimator

52. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 52 Can We Guess the Identity? Identity (“hidden”) variable: z i  {1 (background), 0(topic)} the paper presents a text mining algorithm the paper... z i 1 0 1 0... Suppose the parameters are all known, what’s a reasonable guess of z i ? - depends on (why?) - depends on p(w|C) and p(w|  F ) (how?) E-step Initially, set p(w|  F ) to some random value, then iterate … M-step

53. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 53 An Example of EM Computation Assume =0.5 Expectation-Step: Augmenting data by guessing hidden variables Maximization-Step With the “augmented data”, estimate parameters using maximum likelihood

54. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 54 Example of Feedback Query Model Trec topic 412: “airport security” =0.9 =0.7 Mixture model approach Web database Top 10 docs

55. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 55 Model-based feedback Improves over Simple LM [Zhai & Lafferty 01b] Translation models, Relevance models, and Feedback-based query models have all been shown to improve performance significantly over the simple LMs (Parameter tuning is necessary in many cases, but see [Tao & Zhai 06] for “parameter-free” pseudo feedback)

56. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 56 What Should You Know The KL-divergence retrieval formula as a generalization of the query likelihood method How the mixture model for feedback works Know how to estimate the simple mixture model using EM

57. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 57 Review of LM for IR: Part 4. Language models for special retrieval tasks

58. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 58 Cross-Lingual IR Use query in language A (e.g., English) to retrieve documents in language B (e.g., Chinese) Cross-lingual p(Q|D,R) [Xu et al 01] Cross-lingual p(D|Q,R) [Lavrenko et al 02] English Chinese EnglishChinese word Method 1: Method 2: Translation model Estimate with a bilingual lexicon Or Parallel corpora Estimate with parallel corpora

59. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 59 Distributed IR Retrieve documents from multiple collections The task is generally decomposed into two subtasks: Collection selection and result fusion Using LMs for collection selection [Xu & Croft 99, Si et al. 02] –Treat collection selection as “retrieving collections” as opposed to “documents” –Estimate each collection model by maximum likelihood estimate [Si et al. 02] or clustering [Xu & Croft 99] Using LMs for result fusion [ Si et al. 02] –Assume query likelihood scoring for all collections, but on each collection, a distinct reference LM is used for smoothing –Adjust the bias score p(Q|D,Collection) to recover the fair score p(Q|D)

60. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 60 Structured Document Retrieval [Ogilvie & Callan 03] Title Abstract Body-Part1 Body-Part2 … D D1D1 D2D2 D3D3 DkDk -Want to combine different parts of a document with appropriate weights -Anchor text can be treated as a “part” of a document - Applicable to XML retrieval “part selection” prob. Serves as weight for D j Can be trained using EM Select D j and generate a query word using D j

61. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 61 Personalized/Context-Sensitive Search [Shen et al. 05, Tan et al. 06] User information and search context can be used to estimate a better query model Refinement of this model leads to specific retrieval formulas Simple models often end up interpolating many unigram language models based on different sources of evidence, e.g., short-term search history [Shen et al. 05] or long-term search history [Tan et al. 06] Context-independent Query LM: Context-sensitive Query LM:

62. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 62 Modeling Redundancy Given two documents D 1 and D 2, decide how redundant D 1 (or D 2 ) is w.r.t. D 2 (or D 1 ) Redundancy of D 1  “to what extent can D 1 be explained by a model estimated based on D 2 ” Use a unigram mixture model [Zhai 02] See [Zhang et al. 02] for a 3-component redundancy model Along a similar line, we could measure document similarity in an asymmetric way [Kurland & Lee 05] Maximum Likelihood estimator EM algorithm Reference LM LM for D 2 Measure of redundancy

63. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 63 Predicting Query Difficulty [Cronen-Townsend et al. 02] Observations: –Discriminative queries tend to be easier –Comparison of the query model and the collection model can indicate how discriminative a query is Method: –Define “query clarity” as the KL-divergence between an estimated query model or relevance model and the collection LM –An enriched query LM can be estimated by exploiting pseudo feedback (e.g., relevance model) Correlation between the clarity scores and retrieval performance is found

64. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 64 Expert Finding [Balog et al. 06, Fang & Zhai 07] Task: Given a topic T, a list of candidates {C i }, and a collection of support documents S={D i }, rank the candidates according to the likelihood that a candidate C is an expert on T. Retrieval analogy: –Query = topic T –Document = Candidate C –Rank according to P(R=1|T,C) –Similar derivations to those on slides 55-56, 64 can be made Candidate generation model: Topic generation model:

65. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 65 Summary

66. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 66 SLMs vs. Traditional IR Pros: –Statistical foundations (better parameter setting) –More principled way of handling term weighting –More powerful for modeling subtopics, passages,.. –Leverage LMs developed in related areas –Empirically as effective as well-tuned traditional models with potential for automatic parameter tuning Cons: –Lack of discrimination (a common problem with generative models) –Less robust in some cases (e.g., when queries are semi-structured) –Computationally complex –Empirically, performance appears to be inferior to well-tuned full- fledged traditional methods (at least, no evidence for beating them)

67. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 67 What We Have Achieved So Far Framework and justification for using LMs for IR Several effective models are developed –Basic LM with Dirichlet prior smoothing is a reasonable baseline –Basic LM with informative priors often improves performance –Translation model handles polysemy & synonyms –Relevance model incorporates LMs into the classic probabilistic IR model –KL-divergence model ties feedback with query model estimation –Mixture models can model redundancy and subtopics Completely automatic tuning of parameters is possible LMs can be applied to virtually any retrieval task with great potential for modeling complex IR problems

68. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 68 Challenges and Future Directions Challenge 1: Establish a robust and effective LM that –Optimizes retrieval parameters automatically –Performs as well as or better than well-tuned traditional retrieval methods with pseudo feedback –Is as efficient as traditional retrieval methods Challenge 2: Demonstrate consistent and substantial improvement by going beyond unigram LMs –Model limited dependency between terms –Derive more principled weighting methods for phrases Can LMs consistently (convincingly) outperform traditional methods without sacrificing efficiency? Can we do much better by going beyond unigram LMs?

69. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 69 Challenges and Future Directions (cont.) Challenge 3: Develop LMs that can support “life-time learning” –Develop LMs that can improve accuracy for a current query through learning from past relevance judgments –Support collaborative information retrieval Challenge 4: Develop LMs that can model document structures and subtopics –Recognize query-specific boundaries of relevant passages –Passage-based/subtopic-based feedback –Combine different structural components of a document How can we learn effectively from past relevance judgments? How can we break the document unit in a principled way?

70. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 70 Challenges and Future Directions (cont.) Challenge 5: Develop LMs to support personalized search –Infer and track a user’s interests with LMs –Incorporate user’s preferences and search context in retrieval –Customize/organize search results according to user’s interests Challenge 6: Generalize LMs to handle relational data –Develop LMs for semi-structured data (e.g., XML) –Develop LMs to handle structured queries –Develop LMs for keyword search in relational databases How can we exploit user information and search context to improve search? What role can LMs play when combining text with relational data?

71. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 71 Challenges and Future Directions (cont.) Challenge 7: Develop LMs for hypertext retrieval –Combine LMs with link information –Modeling and exploiting anchor text –Develop a unified LM for hypertext search Challenge 8: Develop LMs for retrieval with complex information needs, e.g., –Subtopic retrieval –Readability constrained retrieval –Entity retrieval (e.g. expert search) How can we exploit LMs to develop models for complex retrieval tasks? How can we develop an effective unified retrieval model for Web search?

72. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 72 What You Should Know General picture of language models for IR The KL-divergence retrieval formula as a generalization of the query likelihood method How the mixture model for feedback works Know how to estimate the simple mixture model using EM

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80. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 80 Roadmap This lecture: systematic review of language models for IR Next lecture: formal retrieval frameworks