We now discuss all these in detail.
given a document d, the simplest way to estimate the document language model is to treat the document as a sample from the underlying multinomial word distribution and use the maximum likelihood estimator: P (w|Θ d ) = c (w,d) |d| , where c(w, d) is the count of word w in document d, and |d| is the length of d. as discussed in virtually all the existing work on using language models for retrieval, such an estimate is problematic and inaccurate; indeed, it would assign zero probability to any word not present in document d, causing problems in scoring a document with query likelihood or KLdivergence (Zhai and Lafferty, 2001b).