2 research outputs found
Remedies against the vocabulary gap in information retrieval
Search engines rely heavily on term-based approaches that represent queries
and documents as bags of words. Text---a document or a query---is represented
by a bag of its words that ignores grammar and word order, but retains word
frequency counts. When presented with a search query, the engine then ranks
documents according to their relevance scores by computing, among other things,
the matching degrees between query and document terms. While term-based
approaches are intuitive and effective in practice, they are based on the
hypothesis that documents that exactly contain the query terms are highly
relevant regardless of query semantics. Inversely, term-based approaches assume
documents that do not contain query terms as irrelevant. However, it is known
that a high matching degree at the term level does not necessarily mean high
relevance and, vice versa, documents that match null query terms may still be
relevant. Consequently, there exists a vocabulary gap between queries and
documents that occurs when both use different words to describe the same
concepts. It is the alleviation of the effect brought forward by this
vocabulary gap that is the topic of this dissertation. More specifically, we
propose (1) methods to formulate an effective query from complex textual
structures and (2) latent vector space models that circumvent the vocabulary
gap in information retrieval.Comment: PhD thesi