Automatic Text Decomposition and Structuring

Sophisticated text similarity measurements are used to determine relationships between natural-language texts and text segments. The resulting linked hypertext maps are used to identify different text types and text structures, leading to improved text access and utilization. Examples of text decomposition are given for expository and non-expository texts. The vector processing model of retrieval has been used with substantial success to manipulate large collections of natural-language text. In vector processing, texts or text excerpts, as well as requests for information, are represented by sets of terms, or term vectors. Collectively the terms assigned to a given text are used to represent text content. Substantially identical methods are usable for determining collection structure (by comparing pairs of text vectors with each other and identifying text pairs found to be suuciently similar), and for retrieving information (by comparing query vectors with the vectors representing the stored items and retrieving items found to be similar to the queries). The results of a similarity computation between a query vector and the stored document vectors can be ranked in decreasing order of the computed query similarity. This makes