Language Models. Hongning Wang

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1 Language Models Hongning Wang

2 Notion of Relevance Relevance (Rep(q), Rep(d)) Similarity P(r1 q,d) r {0,1} Probability of Relevance P(d q) or P(q d) Probabilistic inference Different rep & similarity Vector space model (Salton et al., 75) Regression Model (Fox 83) Prob. distr. model (Wong & Yao, 89) Doc generation Classical prob. Model (Robertson & Sparck Jones, 76) Generative Model Query generation LM approach (Ponte & Croft, 98) (Lafferty & Zhai, 01a) Different inference system Prob. concept space model (Wong & Yao, 95) Inference network model (Turtle & Croft, 91) CS@UVa CS 4501: Information Retrieval 2

3 Query generation models O( R 1 Q, D) P( Q, D R P( Q, D R 1) 0) P( Q D, R 1) P( D R 1) P( Q D, R 0) P( D R 0) P( D R P( Q D, R 1) P( D R 1) 0) ( Assume P( Q D, R 0) P( Q R 0)) Query likelihood p(q θ d ) Document prior Assuming uniform document prior, we have O( R 1 Q, D) P( Q D, R 1) Now, the question is how to compute P( Q D, R 1)? Generally involves two steps: (1) estimate a language model based on D (2) compute the query likelihood according to the estimated model Language models, we will cover it now! CS@UVa CS 4501: Information Retrieval 3

4 What is a statistical LM? A model specifying a probability distribution over word sequences p( Today is Wednesday ) p( Today Wednesday is ) p( The eigenvalue is positive ) It can be regarded as a probabilistic mechanism for generating text, thus also called a generative model CS@UVa CS 4501: Information Retrieval 4

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) CS@UVa CS 4501: Information Retrieval 5

6 Source-Channel framework [Shannon 48] Source P(X) Transmitter Noisy Receiver X (encoder) Channel Y (decoder) X P(Y X) P(X Y)? Destination Xˆ arg max X p( X Y ) arg max X p( Y X ) p( X ) (Bayes Rule) When X is text, p(x) is a language model Many Examples: Speech recognition: XWord sequence YSpeech signal Machine translation: XEnglish sentence YChinese sentence OCR Error Correction: XCorrect word Y Erroneous word Information Retrieval: XDocument YQuery Summarization: XSummary YDocument CS@UVa CS 4501: Information Retrieval 6

7 Language model for text Probability distribution over word sequences pp ww 1 ww 2 ww nn pp ww 1 pp ww 2 ww 1 pp ww 3 ww 1, ww 2 Complexity - OO(VV nn ) nn - maximum document length sentence We need independence assumptions! pp ww nn ww 1, ww 2,, ww nn 1 Chain rule: from conditional probability to joint probability 475,000 main headwords in Webster's Third New International Dictionary Average English sentence length is 14.3 words A rough estimate: OO( ) How large is this? bbbbbbbbbb ee66 TTTT CS@UVa CS 4501: Information Retrieval 7

8 Unigram language model Generate a piece of text by generating each word independently pp(ww 1 ww 2 ww nn ) pp(ww 1 )pp(ww 2 ) pp(ww nn ) ss. tt. pp ww NN ii ii1, ii pp ww ii 1, pp ww ii 0 Essentially a multinomial distribution over the vocabulary A Unigram Language Model The simplest and most popular choice! w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 w15 w16 w17 w18 w19 w20 w21 w22 w23 w24 w25 CS@UVa CS 4501: Information Retrieval 8

9 More sophisticated LMs N-gram language models In general, pp(ww 1 ww 2 ww nn ) pp(ww 1 )pp(ww 2 ww 1 ) pp(ww nn ww 1 ww nn 1 ) N-gram: conditioned only on the past N-1 words E.g., bigram: pp(ww 1 ww nn ) pp(ww 1 )pp(ww 2 ww 1 ) pp(ww 3 ww 2 ) pp(ww nn ww nn 1 ) Remote-dependence language models (e.g., Maximum Entropy model) Structured language models (e.g., probabilistic context-free grammar) CS@UVa CS 4501: Information Retrieval 9

10 Why just unigram models? Difficulty in moving toward more complex models They involve more parameters, so need more data to estimate 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... CS 4501: Information Retrieval 10

11 Language models for IR [Ponte & Croft SIGIR 98] Document Language Model Query Text mining paper text? mining? assocation? clustering? food?? data mining algorithms Food nutrition paper food? nutrition? healthy? diet? Which model would most likely have generated this query? CS 4501: Information Retrieval 11

12 Ranking docs by query likelihood Doc LM Query likelihood d 1 θ d 1 p(q θ d 1 ) q d 2 θ d 2 p(q θ d 2 ) p(q θ d N ) d N θ d N Justification: PRP O( R 1 Q, D) P( Q D, R 1) CS@UVa CS 4501: Information Retrieval 12

13 Justification from PRP 0)) ( 0), ( ( 0) ( 1) ( 1), ( 0) ( 0), ( 1) ( 1), ( 0), ( 1), ( ), 1 ( R Q P R D Q P Assume R D P R D P R D Q P R D P R D Q P R D P R D Q P R D Q P R D Q P D Q R O Query likelihood p(q θ d ) Document prior Assuming uniform document prior, we have 1), ( ), 1 ( R D Q P D Q R O CS@UVa CS 4501: Information Retrieval 13 Query generation

14 Retrieval as language model estimation Document ranking based on query likelihood log p( q d) log where, q i w w w p( w Retrieval problem Estimation of pp(ww ii dd) Common approach Maximum likelihood estimation (MLE) n i d) Document language model CS@UVa CS 4501: Information Retrieval 14

15 Recap: maximum likelihood estimation Data: a document d with counts c(w 1 ),, c(w N ) Model: multinomial distribution p(ww θθ) with parameters θθ ii pp(ww ii ) Maximum likelihood estimator: θθ aaaaaaaaaaxx θθ pp(ww θθ) pp WW θθ NN NN cc ww 1,, cc(ww NN ) ii1 NN NN θθ ii cc(ww ii ) ii1 LL WW, θθ cc ww ii log θθ ii + λλ θθ ii 1 ii1 ii1 LL cc ww ii + λλ θθ θθ ii θθ ii cc ww ii ii λλ Since θθ ii ii1 NN θθ ii cc(ww ii ) NN θθ ii 1 we have λλ cc ww ii cc ww ii NN ii1 log pp WW θθ cc ww ii Using Lagrange multiplier approach, we ll tune θθ ii to maximize LL(WW, θθ) Set partial derivatives to zero ML estimate CS@UVa NN CS 4501: Information Retrieval 15 ii1 cc ww ii NN ii1 Requirement from probability log θθ ii

16 Problem with MLE Unseen events There are 440K tokens on a large collection of Yelp reviews, but: Only 30,000 unique words occurred Word frequency ff kk; ss, NN 1/kk ss Only 0.04% of all possible bigrams occurred This means any word/n-gram that does not occur in the collection has a zero probability with MLE! No future queries/documents can contain those unseen words/n-grams Word rank by frequency A plot of word frequency in Wikipedia (Nov 27, 2006) CS@UVa CS 4501: Information Retrieval 16

17 Problem with MLE What probability should we give a word that has not been observed in the document? log0? If we want to assign non-zero probabilities to such words, we ll have to discount the probabilities of observed words This is so-called smoothing CS@UVa CS 4501: Information Retrieval 17

18 Illustration of language model smoothing P(w d) Max. Likelihood Estimate p ( w) count of w ML count of all words Smoothed LM w Discount from the seen words Assigning nonzero probabilities to the unseen words CS@UVa CS 4501: Information Retrieval 18

19 General idea of smoothing All smoothing methods try to 1. Discount the probability of words seen in a document 2. Re-allocate the extra counts such that unseen words will have a non-zero count CS@UVa CS 4501: Information Retrieval 19

20 Smoothing methods Method 1: Additive smoothing Add a constant δ to the counts of each word cwd (, ) + 1 pw ( d) d + V Problems? Counts of w in d Length of d (total counts) Hint: all words are equally important? Add one, Laplace smoothing Vocabulary size CS@UVa CS 4501: Information Retrieval 20

21 Add one smoothing for bigrams CS 4501: Information Retrieval 21

22 After smoothing Giving too much to the unseen events CS 4501: Information Retrieval 22

23 Refine the idea of smoothing Should all unseen words get equal probabilities? We can use a reference model to discriminate unseen words Discounted ML estimate pseen ( w d) if w is seen in d pw ( d) αd p( w REF) otherwise α d 1 p ( w d) w is seen w is unseen seen p( w REF) Reference language model CS@UVa CS 4501: Information Retrieval 23

24 Smoothing methods Method 2: Absolute discounting Subtract a constant δ from the counts of each word u p( w d) max( c( w; d ) δ,0) + δ d p( w REF ) d Problems? Hint: varied document length? # uniq words CS@UVa CS 4501: Information Retrieval 24

25 Smoothing methods Method 3: Linear interpolation, Jelinek- Mercer Shrink uniformly toward p(w REF) cwd (, ) p( w d) (1 λ) + λ p( w REF) d Problems? MLE Hint: what is missing? parameter CS@UVa CS 4501: Information Retrieval 25

26 Smoothing methods Method 4: Dirichlet Prior/Bayesian Assume pseudo counts µp(w REF) c( w; d ) + µ p( w REF ) d cwd (, ) µ p ( w d) d + µ d + µ + d + µ p( w REF) d Problems? parameter CS@UVa CS 4501: Information Retrieval 26

27 Dirichlet prior smoothing Bayesian estimator Posterior of LM: pp θθ dd pp dd θθ pp(θθ) Conjugate prior Posterior will be in the same form as prior Prior can be interpreted as extra / pseudo data Dirichlet distribution is a conjugate prior for multinomial distribution Dir( θ α,, α 1 N ) likelihood of doc given the model Γ( α + + α N 1 N α i 1 Πθ i Γ( α 1 1) Γ( α ) i N extra / pseudo word counts, we set α i µ p(w i REF) ) prior over models CS@UVa CS 4501: Information Retrieval 27

Some background knowledge Conjugate prior Posterior dist in the same family as prior Dirichlet distribution Continuous Samples from it will be

28 Some background knowledge Conjugate prior Posterior dist in the same family as prior Dirichlet distribution Continuous Samples from it will be the parameters in a multinomial distribution Gaussian -> Gaussian Beta -> Binomial Dirichlet -> Multinomial CS@UVa CS 4501: Information Retrieval 28

29 Dirichlet prior smoothing (cont) Posterior distribution of parameters: p θ d ) Dir( θ c( w ) + α,, c( ) + α ( 1 1 w N N ) Property : If θ ~ Dir( θ α ), then E( θ ) { The predictive distribution is the same as the mean: p(w i ˆ) θ p(w c(w d i i + θ ) Dir( θ α ) dθ ) + α N i 1 α i i c(w i ) + µ p( wi d + µ α i α i } REF ) Dirichlet prior smoothing CS@UVa CS 4501: Information Retrieval 29

30 Estimating µ using leave-one-out [Zhai & Lafferty 02] w 1 w 2 P(w 1 d - w1 ) P(w 2 d - w2 ) Leave-one-out log-likelihood c( w, d ) 1+ µ p( w C) L N i ( µ C) c( w, di )log( 1 i 1 w V di 1+ µ ) w n... Maximum Likelihood Estimator μˆ argmax L 1(μ C) μ P(w n d - wn ) CS@UVa CS 4501: Information Retrieval 30

31 Understanding smoothing Topical words Query the algorithms for data mining p ML (w d1): p ML (w d2): p( algorithms d1) p( algorithms d2) p( data d1) < p( data d2) p( mining d1) < p( mining d2) Intuitively, d2 should have a higher score, but p(q d1)>p(q d2) So we should make p( the ) and p( for ) less different for all docs, and smoothing helps to achieve this goal After smoothing with p( w d) 0.1p ( w d) p( w REF), p( q d1) < DML p( q d2)! Query the algorithms for data mining P(w REF) Smoothed p(w d1): Smoothed p(w d2): CS@UVa CS 4501: Information Retrieval 31

32 Two-stage smoothing [Zhai & Lafferty 02] Stage-1 -Explain unseen words -Dirichlet prior (Bayesian) µ Stage-2 -Explain noise in query -2-component mixture λ P(w d) (1-λ) c(w,d) d +µp(w C) +µ Collection LM + λp(w U) User background model CS@UVa CS 4501: Information Retrieval 32

33 Smoothing & TF-IDF weighting log pq ( d) cwq (, ) log pw ( d) w V, c( wq, ) > 0 cwq (, ) log p ( w d) + cwq (, ) log α pw ( C) Seen w V, c( wd, ) > 0, w V, c( wq, ) > 0, c( wd, ) 0 c( wq, ) > 0 Retrieval formula using the general smoothing scheme cwq (, ) log p ( w d) + cwq (, ) log α pw ( C) cwq (, ) log α pw ( C) Seen d d w V, c( wd, ) > 0 w V, c( wq, ) > 0 w V, c( wq, ) > 0, c( wd, ) > 0 cc ww, qq > 0 pseen ( w d) cwq (, ) log q log αd cwqpw (, ) ( C) w V, c( wd, ) > 0 αd pwc ( ) w V, c( wq, ) > 0 c( wq, ) > α d pseen ( w d) if w is seen in d pw ( d) αd p( w C) otherwise 1 p ( w d) w is seen w is unseen Seen pwc ( ) d Smoothed ML estimate Reference language model Key rewriting step (where did we see it before?) Similar rewritings are very common when using probabilistic models for IR CS@UVa CS 4501: Information Retrieval 33

34 Understanding smoothing αd Plug in the general smoothing scheme to the query likelihood retrieval formula, we obtain TF weighting 1 p ( w d) w is seen w is unseen seen p( w REF) Doc length normalization (longer doc is expected to have a smaller α d ) log p( q d) w i d q p [log α seen d ( w p( w i i d) ] + C) q logα d + w q i log p( w i C) IDF weighting Ignore for ranking Smoothing with p(w C) TF-IDF + doclength normalization CS@UVa CS 4501: Information Retrieval 34

35 What you should know How to estimate a language model General idea and different ways of smoothing Effect of smoothing CS@UVa CS 4501: Information Retrieval 35

36 Today s reading Introduction to information retrieval Chapter 12: Language models for information retrieval CS@UVa CS 4501: Information Retrieval 36

Ranked Retrieval (2)

Text Technologies for Data Science INFR11145 Ranked Retrieval (2) Instructor: Walid Magdy 31-Oct-2017 Lecture Objectives Learn about Probabilistic models BM25 Learn about LM for IR 2 1 Recall: VSM & TFIDF