Enriching Temporal Query Understanding through Date Identification: How to Tag Implicit Temporal Queries?

dc.contributor.author Ricardo Campos en
dc.contributor.author Alípio Jorge en
dc.contributor.author Célia Nunes en
dc.contributor.author Gaël Dias en
dc.date.accessioned 2017-11-16T14:18:35Z
dc.date.available 2017-11-16T14:18:35Z
dc.date.issued 2012 en
dc.description.abstract Generically, search engines fail to understand the user's temporal intents when expressed as implicit temporal queries. This causes the retrieval of less relevant information and prevents users from being aware of the possible temporal dimension of the query results. In this paper, we aim to develop a language-independent model that tackles the temporal dimensions of a query and identifies its most relevant time periods. For this purpose, we propose a temporal similarity measure capable of associating a relevant date(s) to a given query and filtering out irrelevant ones. Our approach is based on the exploitation of temporal information from web content, particularly within the set of k-top retrieved web snippets returned in response to a query. We particularly focus on extracting years, which are a kind of temporal information that often appears in this type of collection. We evaluate our methodology using a set of real-world text temporal queries, which are clear concepts (i.e. querie en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/2923
dc.identifier.uri http://dx.doi.org/10.1145/2169095.2169103 en
dc.language eng en
dc.relation 4981 en
dc.relation 5782 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Enriching Temporal Query Understanding through Date Identification: How to Tag Implicit Temporal Queries? en
dc.type conferenceObject en
dc.type Publication en
Files