Information retrieval addresses the problem of finding those documents whose content matches a user's request from among a large collection of documents. Currently, the most successful general purpose retrieval methodsare statistical methods that treat text as little more than a bag of words. However, attempts to improve retrieval performance through more sophisticated linguistic processing have been largely unsuccessful. Indeed, unless done carefully, such processing can degrade retrieval effectiveness.Several factors contribute to the difficulty of improving on a good statistical baseline including: the forgiving nature but broad coverage of the typical retrieval task; the lack of good weighting schemes for compound index terms; and the implicit linguistic processing inherent in the statistical methods. Natural language processing techniques may be more important for related tasks such as question answering or document summarization.
Citation: Summer School on Information Extraction
Pub Type: Journals
information retrieval, natural language processing, text retrieval