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Language Models Naama Kraus (Modified by Amit Gross) Slides are based on Introduction to Information Retrieval Book by Manning, Raghavan and Schütze.

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Presentation on theme: "Language Models Naama Kraus (Modified by Amit Gross) Slides are based on Introduction to Information Retrieval Book by Manning, Raghavan and Schütze."— Presentation transcript:

1 Language Models Naama Kraus (Modified by Amit Gross) Slides are based on Introduction to Information Retrieval Book by Manning, Raghavan and Schütze

2 IR approaches Boolean retrieval – Boolean constrains of term occurrences in documents – no ranking Vector space model – Queries and vectors are represented as vectors in a high dimensional space – Notions of similarity (cosine similarity) implying ranking Probabilistic model – Rank documents by the probability P(R|d,q) – Estimate P(R|d,q) using relevance feedback technique Language Models – today’s class

3 Intuition Users who try to think of a good query, think of words that are likely to appear in relevant documents Language model approach: A document is a good match to a query, if the document model is likely to generate the query – If document contains query words often

4 Illustration Language Model document query

5 Traditional language model Finite automata Generative model I wish I wish I wish I wish I wish …… The language of the automaton: the full set of strings that it can generate

6 Probabilistic language model Each node has a probability distribution over generating different terms A language model is a function that puts a probability measure over strings drawn from some vocabulary The model is called Finite State Transducer

7 Language model example s the0.2 a0.1 frog0.01 toad0.01 said0.03 likes0.02 that0.04 ….. STOP0.2 state emission probabilities (partial) unigram language model P(frog said that toad likes frog) = 0.01 x 0.03 x 0.04 x 0.01 x 0.02 x 0.01 (We ignore continue/stop probabilities assuming they are fixed for all queries) Probability that some text (e.g. a query) was generated by the model:

8 Query likelihood sfrogsaidthattoadlikesthatdog M10. M20.00020.030.040.00010.04 0.01 q = frog likes toad P(q | M1) = 0.01 x 0.02 x 0.01 P(q | M2) = 0.0002 x 0.04 x 0.0001 P(q|M1) > P(q|M2) => M1 is more likely to generate query q

9 Types of language models How do we build probabilities over sequence of terms? P(t1 t2 t3 t4) = P(t1) x P(t2|t1) x P(t3|t1 t2) x P(t4|t1 t2 t3) Unigram language model – most simplest ; no conditioning context P(t1 t2 t3 t4) = P(t1) x P(t2) x P(t3) x P(t4) Bigram language model – condition on previous term P(t1 t2 t3 t4) = P(t1) x P(t2|t1) x P(t3|t2) x P(t4|t3) Trigram language model … Unigram model is the most common in IR Often sufficient to judge the topic of a document Data sparseness issues when using richer models Simple and efficient implementation

10 The query likelihood model Goal: rank documents by P(d|q) – The probability that a user querying q, had the document d in mind Bayes Rule: P(d|q) = P(q|d)P(d)/P(q) P(q) – same for all documents  ignored P(d) – often treated as uniform across documents  ignored – Could be non uniform prior based on criteria like authority, length, genre, newness …  Rank by P(q|d)

11 The query likelihood model (2) P(q|d) - the probability that a query q was generated by a language model derived from document d – The probability that a query would be observed as a random sample from the respective document model Algorithm: 1.Infer a Language Model Md for each document d 2.Estimate P(q|Md) 3.Rank the documents according to these probabilities

12 Illustration d1 Md1 query d2 Md2 d3 Md3 P(q|Md1) P(q|Md2) P(q|Md3) E.g., P(q|Md3) > P(q|Md1) > P(q|Md2)  d3 is first, d1 is second, d2 is third

13 Estimating P(q|Md) Use Maximum Likelihood Estimation - MLE Assume a unigram language model (terms occur independently) unigramMLE Length of document Term frequency

14 Sparse data problem Documents are sparse – Some words don’t appear in the document – In particular, some of the query terms  P(q|d) = 0 ; zero probability problem – Conjunctive semantics Occurring words are poorly estimated – A single documents is small training set – Occurring words are over estimated Their occurrence was partly by chance

15 Solution: smoothing Smooth probabilities in Language Models – overcome zero probabilities – give some probability mass to unseen words The probability of a non occurring term should be close to its probability to occur in the collection P(t|Mc) = cf(t)/T cf(t) = #occurrences of term t in the collection T – length of the collection = sum of all document lengths

16 Smoothing methods Linear Interpolation Bayesian smoothing Summary, with linear interpolation In practice, log in taken from both sides of the equation to avoid multiplying many small numbers

17 Exercise Given a collection of two documents D1, D2 D1: Xyzzy reports a profit but revenue is down D2: Quorus narrows quarter loss but revenue decreases further A user submitted the query: “revenue down” Rank D1 and D2 - Use an MLE unigram model and a linear interpolation smoothing with lambda parameter 0.5

18 Extended LM approaches query model document Document model query likelihood document likelihood model comparison Query likelihood P(q|d) – the probability of document LM to generate query we’ve seen in previous slides … Document likelihood P(d|q) – the probability of query LM to generate document in the next slides … Model comparison R(d;q) – compare between document and query models in the next slides … P(t|query) P(t|document)

19 Document likelihood model P(d|q) – the probability of query LM to generate document Problem: queries are short  bad model estimation [Zhai and Lafferty 2001] – Expand the query with terms taken from relevant documents in the usual way and hence update the language mode

20 KL divergence Kullback–Leibler (KL) divergence An asymmetric divergence measure from information theory Measures the difference between two probability distributions P, Q Typically Q is an estimation of P Properties Non negative Equals 0 iff P equals Q May have an infinite value Asymmetric, thus not a metric Jensen–Shannon (JS) divergence Based on KL divergence (D) Always finite 0 <= JSD <= 1 Symmetric

21 Model comparison Make LM from both query and document Measure `how different` these LMs from each other  Use KL divergence Rank by KLD - the closer to 0 the higher is the rank

22 Language models - summary Probabilistic model – mathematically precise Intuitive, simple concept Achieves very good retrieval results – Still, no evidence that it exceeds the traditional vector space model Relation to the Vector Space Model – Both use term frequency – Smoothing with collection generation probability is a little like idf Terms rare in the general collection but common in some documents will have a greater influence on the document’s ranking – Probabilistic vs. geometric – Mathematical mode vs. heuristic model

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