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Noise words

From the earliest days of IR (e.g. Luhn's seminal work [Luhn57] ) two related facts have been obvious: first, that a relatively small number of words account for a very significant fraction of all text's bulk. Words like IT, AND and TO can be found in virtually every sentence. Second, these NOISE WORDS make very poor index terms. Users are unlikely to ask for documents about TO and it is hard to imagine a document about BE.} Due then to both their frequency and their lack of indexing consequence, we will build in the capability of ignoring noise words into our lexical analyzer.

As will be discussed extensively in Chapter 3, noise words are often imagined to be the most frequently occurring words in a corpus. One problem with defining noise words in this way is that it requires a frequency analysis of the corpus prior to lexical analysis. It is possible to use frequency analyses from other corpora, assuming that the distribution of noise words is relatively constant across corpora, but such an extrapolation is not always warranted. Worse, the most frequent words often include words that might make very good key words. Fox notes that the words TIME, WAR, HOME, LIFE, WATER and WORLD are among the 200 most frequent in general English literature.[FoxC92]

Instead, we will define noise words extensionally, in terms of a finite list or NEGATIVE DICTIONARY. The list we use, STOP.WRD, was derived by Fox from an analysis of the Brown corpus[Fox90] .

The relationship between these noise words and those words most critical to syntactic analysis of natural language sentences is striking. note that the same tokens that are thrown away as noise because they have no meaning are precisely those FUNCTION WORDS most important to the syntactic analysis of well-formed sentences. This is the first, but not the last, suggestion of a fundamental complimentarity between FOA's concern with semantics and computational linguistics' concern with syntax.


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