This paper addresses the problem of enhancing document retrieval under the specific restrictions posed by the Question Answering scenario. In particular, given an input question, we aim at defining a reliable method for expand ing its keywords with semantic information extracted from WordNet (e.g. synonyms or hypernyms). This is a challenging task, since it is intrinsically dependent on high quality disambiguation of natural language questions which so far has been out of the reach of state-of-the-art Word Sense Disambiguation too ls. The proposed solution relies on a two-step access to the target document collection, and can be seen as a ``sense-based` relevance feedback. According to this technique, once the top d_1,d_2,...,d_n documents have been retrieved using the question keywords, the most frequent senses of the question terms are considered instead of drawing for expansion the most relevant words that appear within d_1,d_2,...,d_n. Query enrichment is then carried out adding terms semantically related to these senses. Our experiments, carried out using part of the TREC-2003 factoid questions set and th e AQUAINT corpus for document retrieval, demonstrate the viability of this approach. Preliminary results show that the application of Sense-based Relevance Feed back to the QA scenario can improve retrieval up to 7% in terms of answer-bearing documents obtained with the best performing expansion strategy.
Sense-based Blind Relevance Feedback for Question Answering
Negri, Matteo
2004-01-01
Abstract
This paper addresses the problem of enhancing document retrieval under the specific restrictions posed by the Question Answering scenario. In particular, given an input question, we aim at defining a reliable method for expand ing its keywords with semantic information extracted from WordNet (e.g. synonyms or hypernyms). This is a challenging task, since it is intrinsically dependent on high quality disambiguation of natural language questions which so far has been out of the reach of state-of-the-art Word Sense Disambiguation too ls. The proposed solution relies on a two-step access to the target document collection, and can be seen as a ``sense-based` relevance feedback. According to this technique, once the top d_1,d_2,...,d_n documents have been retrieved using the question keywords, the most frequent senses of the question terms are considered instead of drawing for expansion the most relevant words that appear within d_1,d_2,...,d_n. Query enrichment is then carried out adding terms semantically related to these senses. Our experiments, carried out using part of the TREC-2003 factoid questions set and th e AQUAINT corpus for document retrieval, demonstrate the viability of this approach. Preliminary results show that the application of Sense-based Relevance Feed back to the QA scenario can improve retrieval up to 7% in terms of answer-bearing documents obtained with the best performing expansion strategy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
