Information Retrieval (IR) is concerned with locating documents that are relevant for a user's information need or query from a large collection of documents. A fundamental problem for information retrieval is word mismatch. A query is usually a short and incomplete description of the underlying information need. The users of IR systems and the authors of the documents often use different words to refer to the same concepts. This thesis addresses the word mismatch problem through automatic text analysis. We investigate two text analysis techniques, corpus analysis and local context analysis, and apply them in two domains of word mismatch, stemming and general query expansion. Experimental results show that these techniques can result in more effective retrieval.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:dissertations-2901 |
| Date | 01 January 1997 |
| Creators | Xu, Jinxi |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Language | English |
| Detected Language | English |
| Type | text |
| Source | Doctoral Dissertations Available from Proquest |
Page generated in 0.003 seconds