We present a novel approach to query reformulation which combines syntactic and semantic information by means of generalized Levenshtein distance algorithms where the substitution...
Amac Herdagdelen, Massimiliano Ciaramita, Daniel M...
People are seldom aware that their search queries frequently mismatch a majority of the relevant documents. This may not be a big problem for topics with a large and diverse set o...
Abstract. Motivated by research on how topology may be a helpful foundation for building information modeling (BIM), a relational database version of the notions of chain complex a...
We explore the utility of different types of topic models for retrieval purposes. Based on prior work, we describe several ways that topic models can be integrated into the retrie...
Recent work in supervised learning of term-based retrieval models has shown significantly improved accuracy can often be achieved via better model estimation [2, 10, 11, 17]. In ...