Inferring query models by computing information flow.
Bruza, Peter D.
MetadataShow full item record
The language modelling approach to information retrieval can also be used to compute query models. A query model can be envisaged as an expansion of an initial query. The more prominent query models in the literature have a probabilistic basis, that is, for each term w in the vocabulary, the probability of w, given the query Q,, is computed. This paper introduces an alternative, nonprobabilistic approach to query modelling whereby the strength of information flow is computed between the query Q and the term w. Information flow is a reflection of how strongly w is informationally contained within the query Q. In other words, the basis of the query model generation is information inference. The information flow model is based on Hyperspace Analogue to Language (HAL) vector representations, which reflects the lexical cooccurrence information of terms. Research from cognitive science has demonstrated the cognitive compatibility of HAL representations with human processing, and therefore HAL vectors would thus seem to be a potentially useful basis for inferring query expansion terms. Query models computed from TREC queries by HAL-based information flow are compared experimentally with two probabilistic query language models. Experimental results are provided showing the HAL-based information flow model be superior to query models computed via Markov chains, and seems to be as effective as a probabilistically motivated relevance model.