BIT and Purdue at TREC-KBA-CCR Track 2014

Jingang Wang, Ning Zhang, Zhiwei Zhang, Dandan Song, Luo Si, Lejian Liao

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

This report summarizes our participation at KBA-CCR track in TREC 2014. Our submissions are generated in two steps: (1) Filtering a candidate documents collection from the stream corpus for a set of target entities; and (2) Estimating the relevance levels between candidate documents and target entities. Three kinds of approaches are employed in the second step, including query expansion, classification and learning to rank. Query expansion is an unsupervised baseline by combining an entity and its related entities as a query to retrieve its relevant documents. Query expansion performs considerably well in vital + useful scenario. It’s not difficult to filter a relevant document set from the stream corpus. However, in vital only scenario, supervised approaches are more powerful than query expansion in identifying vital documents for target entities. Our results reveal that learning to rank approaches are more suitable for CCR with current evaluation methodology.

Original languageEnglish
Publication statusPublished - 2014
Event23rd Text REtrieval Conference, TREC 2014 - Gaithersburg, United States
Duration: 19 Nov 201421 Nov 2014

Conference

Conference23rd Text REtrieval Conference, TREC 2014
Country/TerritoryUnited States
CityGaithersburg
Period19/11/1421/11/14

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