When used in information retrieval, a language model is associated with a document in a collection. With query Q as input, retrieved documents are ranked based on the probability that the document's language model would generate the terms of the query, P(Q|Md). The method to use language models in information retrieval is the query likelihood model.
In practice, unigram language models are most commonly used in information retrieval, as they are sufficient to determine the topic from a piece of text. Unigram models only calculate the probability of hitting an isolated word, without considering any influence from the words before or after the target. This leads to the Bag of words model, and turns out to generate a multinomial distribution over words.
Estimating the probability of sequences can become difficult in corpora, in which phrases or sentences can be arbitrarily long and hence some sequences are not observed during training of the language model (data sparseness problem of overfitting). For that reason these models are often approximated using smoothed N-gram models.
In this model, the probability to hit each word all depends on its own, so we only have one-state finite automata as units. For each automaton, we only have one way to hit its only state, assigned with one probability. Viewing from the whole model, the sum of all the one-state-hitting probabilities should be 1. Followed is an illustration of an unigram model of a document.
|Terms||Probability in doc|
The probability generated for a specific query is calculated as
For different documents, we can build their own unigram models, with different hitting probabilities of words in it. And we use probabilities from different documents to generate different hitting probabilities for a query. Then we can rank documents for a query according to the generating probabilities. Next is an example of two unigram models of two documents.
|Terms||Probability in Doc1||Probability in Doc2|
In information retrieval contexts, unigram language models are often smoothed to avoid instances where . A common approach is to generate a maximum-likelihood model for the entire collection and linearly interpolate the collection model with a maximum-likelihood model for each document to create a smoothed document model.
In an n-gram model, the probability of observing the sentence is approximated as
Here, it is assumed that the probability of observing the ith word wi in the context history of the preceding i-1 words can be approximated by the probability of observing it in the shortened context history of the preceding n-1 words (nth order Markov property).
The conditional probability can be calculated from n-gram frequency counts:
The words bigram and trigram language model denote n-gram language models with n=2 and n=3, respectively.
Typically, however, the n-gram probabilities are not derived directly from the frequency counts, because models derived this way have severe problems when confronted with any n-grams that have not explicitly been seen before. Instead, some form of smoothing is necessary, assigning some of the total probability mass to unseen words or N-grams. Various methods are used, from simple "add-one" smoothing (assign a count of 1 to unseen N-grams) to more sophisticated models, such as Good-Turing discounting or back-off models.
In a bigram (n=2) language model, the probability of the sentence I saw the red house is approximated as
whereas in a trigram (n=3) language model, the approximation is
Note that the context of the first N-grams is filled with start-of-sentence markers, typically denoted <s>.
Additionally, without an end-of-sentence marker, the probability of an ungrammatical sequence *I saw the would always be higher than that of the longer sentence I saw the red house.
A positional language model is one that describes the probability of given words occurring close to one another in a text, not necessarily immediately adjacent. Similarly, bag-of-concepts models leverage on the semantics associated with multi-word expressions such as buy_christmas_present, even when they are used in information-rich sentences like "today I bought a lot of very nice Christmas presents".
- Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: An Introduction to Information Retrieval, pages 237-240. Cambridge University Press, 2009
- Buttcher, Clarke, and Cormack. Information Retrieval: Implementing and Evaluating Search Engines. pg. 289-291. MIT Press.
- Craig Trim, What is Language Modeling?, April 26th, 2013.
- Yuanhua Lv and ChengXiang Zhai, Positional Language Models for Information Retrieval, in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (SIGIR), 2009.
- E. Cambria and A. Hussain. Sentic Computing: Techniques, Tools, and Applications. Dordrecht, Netherlands: Springer, ISBN 978-94-007-5069-2 (2012)
- J M Ponte and W B Croft (1998). "A Language Modeling Approach to Information Retrieval". Research and Development in Information Retrieval. pp. 275–281. CiteSeerX: 10.1.1.117.4237.
- F Song and W B Croft (1999). "A General Language Model for Information Retrieval". Research and Development in Information Retrieval. pp. 279–280. CiteSeerX: 10.1.1.21.6467.
- Chen, Stanley; Joshua Goodman (1998). An Empirical Study of Smoothing Techniques for Language Modeling (Technical report). Harvard University. CiteSeerX: 10.1.1.131.5458, number 10-98.
- DALM - Fast, Free software for language model queries
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- Kylm (Kyoto Language Modeling Toolkit) - Free language modeling toolkit in Java
- KenLM - Fast, Free software for language modeling
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- MITLM - MIT Language Modeling toolkit. Free software
- OpenGrm NGram library - Free software for language modeling. Built on OpenFst.
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- Positional Language Model
- RandLM - Free software for randomised language modeling
- RNNLM - Free recurrent neural network language model toolkit
- SRILM - Proprietary software for language modeling
- VariKN - Free software for creating, growing and pruning Kneser-Ney smoothed n-gram models.
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