DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Dongwoo | ko |
dc.contributor.author | Wang, Haixun | ko |
dc.contributor.author | Oh, Alice Haeyun | ko |
dc.date.accessioned | 2015-06-03T06:09:05Z | - |
dc.date.available | 2015-06-03T06:09:05Z | - |
dc.date.created | 2015-05-27 | - |
dc.date.created | 2015-05-27 | - |
dc.date.created | 2015-05-27 | - |
dc.date.issued | 2013-08-03 | - |
dc.identifier.citation | international joint conference on Artificial Intelligence , pp.2654 - 2661 | - |
dc.identifier.uri | http://hdl.handle.net/10203/198677 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.publisher | International Joint Conferences on Artificial Intelligence Organization (IJCAI) | - |
dc.title | Context-dependent conceptualization | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-84896063773 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 2654 | - |
dc.citation.endingpage | 2661 | - |
dc.citation.publicationname | international joint conference on Artificial Intelligence | - |
dc.identifier.conferencecountry | CC | - |
dc.identifier.conferencelocation | Beijing | - |
dc.contributor.localauthor | Oh, Alice Haeyun | - |
dc.contributor.nonIdAuthor | Wang, Haixun | - |
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