Estimating the query generation probability

In this section we describe how to estimate . The probability
of producing the query given the LM of document using
*maximum likelihood estimation* ( *MLE* ) and
the unigram assumption is:

The classic problem with using language models is one of estimation
(the
symbol on the P's is used above to stress that the model is estimated): terms
appear very *sparsely* in documents. In particular,
some words will not have appeared in the document at all, but are
possible words for the information need, which the user may have used in
the query. If we estimate
for a term missing from a
document , then we get a strict conjunctive semantics: documents will
only give a query non-zero probability if all of the query terms appear
in the document. Zero probabilities are clearly a problem in other
uses of language models, such as when predicting the next word in a
speech recognition application, because many words will be sparsely
represented in the training data. It may seem rather less clear
whether this is problematic in an IR application. This could be
thought of as a
human-computer interface issue: vector space systems have generally
preferred more lenient matching, though recent web search developments
have tended more in the direction of doing searches with such
conjunctive semantics. Regardless of the approach here, there is a
more general problem of estimation: occurring words are also badly
estimated; in particular, the probability of words occurring once in the
document is normally overestimated, since their one occurrence was
partly by chance. The answer to this (as we saw in
probtheory) is smoothing. But as people have come to
understand the LM approach better, it has become apparent that the
role of smoothing in this model is not only to avoid zero
probabilities. The smoothing of terms actually implements major parts of
the term weighting component (Exercise 12.2.3 ). It is
not just that an unsmoothed model
has conjunctive semantics; an unsmoothed model works badly because it
lacks parts of the term weighting component.

Thus, we need to smooth
probabilities in our document language models: to discount non-zero
probabilities and to give some
probability mass to unseen words.
There's a wide space of approaches to smoothing probability
distributions to deal with this problem. In Section 11.3.2 (page ),
we already discussed adding a number (1,
1/2, or a small ) to the observed counts and renormalizing to
give a probability distribution.^{}In this section we will mention a
couple of other smoothing methods, which involve combining observed counts with a
more general reference probability distribution.
The general approach is that a non-occurring term should be
possible in a query, but its probability should be somewhat close to
but no more likely than would be expected by
chance from the whole collection. That is, if
then

(101) |

where and is a language model built from the entire document collection. This mixes the probability from the document with the general collection frequency of the word. Such a model is referred to as a

An alternative is to use a language model built from the whole
collection as a prior distribution
in a *Bayesian updating process*
(rather than a uniform distribution, as we saw in
Section 11.3.2 ). We then get the following equation:

(103) |

Both of these smoothing methods have been shown to perform well in IR experiments; we will stick with the linear interpolation smoothing method for the rest of this section. While different in detail, they are both conceptually similar: in both cases the probability estimate for a word present in the document combines a discounted MLE and a fraction of the estimate of its prevalence in the whole collection, while for words not present in a document, the estimate is just a fraction of the estimate of the prevalence of the word in the whole collection.

The role of smoothing in LMs for IR is not simply or principally to avoid estimation problems. This was not clear when the models were first proposed, but it is now understood that smoothing is essential to the good properties of the models. The reason for this is explored in Exercise 12.2.3 . The extent of smoothing in these two models is controlled by the and parameters: a small value of or a large value of means more smoothing. This parameter can be tuned to optimize performance using a line search (or, for the linear interpolation model, by other methods, such as the expectation maximimization algorithm; see modelclustering). The value need not be a constant. One approach is to make the value a function of the query size. This is useful because a small amount of smoothing (a ``conjunctive-like'' search) is more suitable for short queries, while a lot of smoothing is more suitable for long queries.

To summarize, the retrieval ranking for a query under the basic LM
for IR we have been considering is given by:

**Worked example.**
Suppose the document collection contains two documents:

- : Xyzzy reports a profit but revenue is down
- : Quorus narrows quarter loss but revenue decreases further

Suppose the query is *revenue down*. Then:

(105) | |||

(106) | |||

(107) | |||

(108) |

So, the ranking is .

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2009-04-07