Language model

A statistical language model assigns a probability to a sequence of m words $P(w_1,\ldots,w_m)$ by means of a probability distribution. Having a way to estimate the relative likelihood of different phrases is useful in many natural language processing applications. Language modeling is used in speech recognition, machine translation, part-of-speech tagging, parsing, handwriting recognition, information retrieval and other applications.

In speech recognition, the computer tries to match sounds with word sequences. The language model provides context to distinguish between words and phrases that sound similar. For example, in American English, the phrases "recognize speech" and "wreck a nice beach" are pronounced the same but mean very different things. These ambiguities are easier to resolve when evidence from the language model is incorporated with the pronunciation model and the acoustic model.

Language models are used in information retrieval in the query likelihood model. Here a separate language model is associated with each document in a collection. Documents are ranked based on the probability of the query $Q$ in the document's language model $P(Q|M_d)$. Commonly, the unigram language model is used for this purpose—otherwise known as the bag of words model.

Data sparsity is a major problem in building language models. Most possible word sequences will not be observed in training. One solution is to make the assumption that the probability of a word only depends on the previous $n$ words. This is known as an N-gram model or unigram model when $n=1$.

Unigram models

A unigram model used in information retrieval can be treated as the combination of several one-state finite automata.[1] It splits the probabilities of different terms in a context, e.g. from $P(t_1t_2t_3)=P(t_1)P(t_2|t_1)P(t_3|t_1t_2)$ to $P_{uni}(t_1t_2t_3)=P(t_1)P(t_2)P(t_3)$.

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
a 0.1
world 0.2
likes 0.05
we 0.05
share 0.3
... ...

$\sum_{term\ in\ doc} P(term) = 1$

The probability generated for a specific query is calculated as

$P(query) = \prod_{term\ in\ query} P(term)$

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
a 0.1 0.3
world 0.2 0.1
likes 0.05 0.03
we 0.05 0.02
share 0.3 0.2
... ... ...

In information retrieval contexts, unigram language models are often smoothed to avoid instances where $P(term) = 0$. 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.[2]

N-gram models

Main article: N-gram

In an n-gram model, the probability $P(w_1,\ldots,w_m)$ of observing the sentence $w_1,\ldots,w_m$ is approximated as

$P(w_1,\ldots,w_m) = \prod^m_{i=1} P(w_i|w_1,\ldots,w_{i-1}) \approx \prod^m_{i=1} P(w_i|w_{i-(n-1)},\ldots,w_{i-1})$

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: $P(w_i|w_{i-(n-1)},\ldots,w_{i-1}) = \frac{count(w_{i-(n-1)},\ldots,w_{i-1},w_i)}{count(w_{i-(n-1)},\ldots,w_{i-1})}$

The words bigram and trigram language model denote n-gram language models with n=2 and n=3, respectively.[3]

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.

Example

In a bigram (n=2) language model, the probability of the sentence I saw the red house is approximated as

$P(I,saw,the,red,house) \approx P(I|) P(saw|I) P(the|saw) P(red|the) P(house|red) P(|house)$

whereas in a trigram (n=3) language model, the approximation is

$P(I,saw,the,red,house) \approx P(I|,) P(saw|,I) P(the|I,saw) P(red|saw,the) P(house|the,red) P(|red,house)$

Note that the context of the first $n-1$ 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.

Other models

A positional language model[4] 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[5] 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".