Context-Dependent Conceptualization

摘要

Conceptualization seeks to map a short text (i.e.,a word or a phrase) to a set of concepts as a mechanism of understanding text.Most of prior research in conceptualization uses human-crafted knowledge bases that map instances to concepts.Such approaches to conceptualization have the limitation that the mappings are not context sensitive.To overcome this limitation,we propose a framework in which we harness the power of a probabilistic topic model which inherently captures the semantic relations between words.By combining latent Dirichlet allocation,a widely used topic model with Probase,a large-scale probabilistic knowledge base,we develop a corpus-based framework for context-dependent conceptualization.Through this simple but powerful framework,we improve conceptualization and enable a wide range of applications that rely on semantic understanding of short texts,including frame element prediction,word similarity in context,ad-query similarity,and query similarity.