Text retrieval using inference in semantic metanetworks.

摘要

It is well known that semantic networks are relevant to information retrieval. In particular, the notion of semantic distance can be applied to both the indexing and retrieval phases of processing. This dissertation presents techniques to automatically assign weights to network edges and determine semantic distance between arbitrary nodes. This allows word sense disambiguation during document and query indexing by minimizing mutual distance among word senses within a window of words with one or more senses each. A number of insights have led to improved semantic retrieval, where query/document relatedness is inferred, when compared to a baseline. In addition several performance enhancements have been discovered which reduce run-time by three orders of magnitude.; Semantics has also been combined with more traditional lexical approaches such as the vector space model. Preliminary experiments using semantics have not yet produced significant improvement over strictly lexical approaches, but they have led to a method for obtaining both better recall and precision over the standard vector space approach.