Clumping Properties of Content-Bearing Words

Information Retrieval Systems identify content bearing words, and possibly also assign weights, as part of the process of formulating requests. For optimal retrieval ee-ciency, it is desirable that this be done automatically. This paper deenes the notion of serial-clustering of words in text, and explores the value of such clustering as an indicator of a word's bearing content. This approach is exible in the sense that it is sensitive to context: a term may be assessed as content-bearing within one collection, but not another. Our approach, being numerical, may also be of value in assigning weights to terms in requests. Experimental support is obtained from natural text databases in three diierent languages.