Sentence Retrieval with LSI and Topic Identification

This paper presents two sentence retrieval methods. We adopt the task definition done in the TREC Novelty Track: sentence retrieval consists in the extraction of the relevant sentences for a query from a set of relevant documents for that query. We have compared the performance of the Latent Semantic Indexing (LSI) retrieval model against the performance of a topic identification method, also based on Singular Value Decomposition (SVD) but with a different sentence selection method. We used the TREC Novelty Track collections from years 2002 and 2003 for the evaluation. The results of our experiments show that these techniques, particularly sentence retrieval based on topic identification, are valid alternative approaches to other more ad-hoc methods devised for this task.

• Type: Conference
• Year: 2006
• Where: ECIR
• Authors: David Parapar, Alvaro Barreiro
• Keywords: ECIR 2006, Information Technology, Sentence Retrieval, Sentence Retrieval Methods, Topic Identification