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1 Language Models for TR (Lecture for CS410-CXZ Text Info Systems) Feb. 25, 2011 ChengXiang Zhai Department of Computer Science University of Illinois,

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Presentation on theme: "1 Language Models for TR (Lecture for CS410-CXZ Text Info Systems) Feb. 25, 2011 ChengXiang Zhai Department of Computer Science University of Illinois,"— Presentation transcript:

1 1 Language Models for TR (Lecture for CS410-CXZ Text Info Systems) Feb. 25, 2011 ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign

2 2 Text Generation with Unigram LM (Unigram) Language Model  p(w|  ) … text 0.2 mining 0.1 assocation 0.01 clustering 0.02 … food … Topic 1: Text mining … food 0.25 nutrition 0.1 healthy 0.05 diet 0.02 … Topic 2: Health Document Text mining paper Food nutrition paper Sampling

3 3 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 A “text mining paper” (total #words=100) 10/100 5/100 3/100 1/100

4 4 Language Models for Retrieval (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?

5 5 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

6 6 Retrieval as Language Model Estimation Document ranking based on query likelihood Retrieval problem  Estimation of p(w i |d) Smoothing is an important issue, and distinguishes different approaches Document language model

7 7 How to Estimate p(w|d)? Simplest solution: Maximum Likelihood Estimator –P(w|d) = relative frequency of word w in d –What if a word doesn’t appear in the text? P(w|d)=0 In general, what probability should we give a word that has not been observed? If we want to assign non-zero probabilities to such words, we’ll have to discount the probabilities of observed words This is what “smoothing” is about …

8 8 Language Model Smoothing (Illustration) P(w) Word w Max. Likelihood Estimate Smoothed LM

9 9 A General Smoothing Scheme All smoothing methods try to –discount the probability of words seen in a doc –re-allocate the extra probability so that unseen words will have a non-zero probability Most use a reference model (collection language model) to discriminate unseen words Discounted ML estimate Collection language model

10 10 Smoothing & TF-IDF Weighting Plug in the general smoothing scheme to the query likelihood retrieval formula, we obtain Ignore for ranking IDF weighting TF weighting Doc length normalization (long doc is expected to have a smaller  d ) Smoothing with p(w|C)  TF-IDF + length norm.

11 11 Derivation of the Query Likelihood Retrieval Formula Discounted ML estimate Reference language model Retrieval formula using the general smoothing scheme Key rewriting step Similar rewritings are very common when using LMs for IR…

12 12 Three Smoothing Methods (Zhai & Lafferty 01) Simplified Jelinek-Mercer: Shrink uniformly toward p(w|C) Dirichlet prior (Bayesian): Assume pseudo counts  p(w|C) Absolute discounting: Subtract a constant 

13 13 Comparison of Three Methods

14 14 The Need of Query-Modeling (Dual-Role of Smoothing) Verbose queries Keyword queries Why does query type affect smoothing sensitivity?

15 15 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): p DML (w|d2): Query = “the algorithms for data mining” P(w|REF) Smoothed p(w|d1): Smoothed p(w|d2):

16 16 Two-stage Smoothing c(w,d) |d| P(w|d) = +  p(w|C) ++ Stage-1 -Explain unseen words -Dirichlet prior(Bayesian)  (1- )+ p(w|U) Stage-2 -Explain noise in query -2-component mixture User background model and  can be automatically set through statistical estimation

17 17 Automatic 2-stage results  Optimal 1-stage results Average precision (3 DB’s + 4 query types, 150 topics) SK, LK, SV, LV: different types of queries

18 18 What You Should Know The basic idea of ranking docs by query likelihood (“the language modeling approach”) How smoothing is connected with TF-IDF weighting and document length normalization The basic idea of two-stage smoothing


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