Difference between revisions of "Leveraging Fine-Grained Wikipedia Categories for Entity Search"
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== Overview == | == Overview == | ||
Ad-hoc entity search, which is to retrieve a ranked list of relevant entities in response to a query of natural language question, has been widely studied. It has been shown that category matching of entities, especially when matching to fine-grained entity [[categories]], is critical to the performance of entity search. However, the potentials of fine-grained [[Wikipedia categories]], has not been well exploited by existing studies. Based on the observation of how people describe entities of a specific type, authors propose a headword-and-modifier model to deeply interpret both queries and fine-grained entity categories. Probabilistic generative models are designed to effectively estimate the relevance of headwords and modifiers as a pattern-based matching problem, taking the [[Wikipedia]] type taxonomy as an important input to address the ad-hoc representations of concepts/entities in queries. Extensive experimental results on three widely-used test sets: INEX-XER 2009, SemSearch-LS and TREC-Entity, show that method achieves a significant improvement of the entity search performance over the state-of-the-art methods. | Ad-hoc entity search, which is to retrieve a ranked list of relevant entities in response to a query of natural language question, has been widely studied. It has been shown that category matching of entities, especially when matching to fine-grained entity [[categories]], is critical to the performance of entity search. However, the potentials of fine-grained [[Wikipedia categories]], has not been well exploited by existing studies. Based on the observation of how people describe entities of a specific type, authors propose a headword-and-modifier model to deeply interpret both queries and fine-grained entity categories. Probabilistic generative models are designed to effectively estimate the relevance of headwords and modifiers as a pattern-based matching problem, taking the [[Wikipedia]] type taxonomy as an important input to address the ad-hoc representations of concepts/entities in queries. Extensive experimental results on three widely-used test sets: INEX-XER 2009, SemSearch-LS and TREC-Entity, show that method achieves a significant improvement of the entity search performance over the state-of-the-art methods. | ||
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+ | == Embed == | ||
+ | === Wikipedia Quality === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Ma, Denghao; Chen, Yueguo; Chang, Kevin Chen Chuan; Du, Xiaoyong. (2018). "[[Leveraging Fine-Grained Wikipedia Categories for Entity Search]]". International World Wide Web Conferences Steering Committee. DOI: 10.1145/3178876.3186074. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === English Wikipedia === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | {{cite journal |last1=Ma |first1=Denghao |last2=Chen |first2=Yueguo |last3=Chang |first3=Kevin Chen Chuan |last4=Du |first4=Xiaoyong |title=Leveraging Fine-Grained Wikipedia Categories for Entity Search |date=2018 |doi=10.1145/3178876.3186074 |url=https://wikipediaquality.com/wiki/Leveraging_Fine-Grained_Wikipedia_Categories_for_Entity_Search |journal=International World Wide Web Conferences Steering Committee}} | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === HTML === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Ma, Denghao; Chen, Yueguo; Chang, Kevin Chen Chuan; Du, Xiaoyong. (2018). &quot;<a href="https://wikipediaquality.com/wiki/Leveraging_Fine-Grained_Wikipedia_Categories_for_Entity_Search">Leveraging Fine-Grained Wikipedia Categories for Entity Search</a>&quot;. International World Wide Web Conferences Steering Committee. DOI: 10.1145/3178876.3186074. | ||
+ | </nowiki> | ||
+ | </code> |
Revision as of 20:13, 23 February 2021
Authors | Denghao Ma Yueguo Chen Kevin Chen Chuan Chang Xiaoyong Du |
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Publication date | 2018 |
DOI | 10.1145/3178876.3186074 |
Links | Original |
Leveraging Fine-Grained Wikipedia Categories for Entity Search - scientific work related to Wikipedia quality published in 2018, written by Denghao Ma, Yueguo Chen, Kevin Chen Chuan Chang and Xiaoyong Du.
Overview
Ad-hoc entity search, which is to retrieve a ranked list of relevant entities in response to a query of natural language question, has been widely studied. It has been shown that category matching of entities, especially when matching to fine-grained entity categories, is critical to the performance of entity search. However, the potentials of fine-grained Wikipedia categories, has not been well exploited by existing studies. Based on the observation of how people describe entities of a specific type, authors propose a headword-and-modifier model to deeply interpret both queries and fine-grained entity categories. Probabilistic generative models are designed to effectively estimate the relevance of headwords and modifiers as a pattern-based matching problem, taking the Wikipedia type taxonomy as an important input to address the ad-hoc representations of concepts/entities in queries. Extensive experimental results on three widely-used test sets: INEX-XER 2009, SemSearch-LS and TREC-Entity, show that method achieves a significant improvement of the entity search performance over the state-of-the-art methods.
Embed
Wikipedia Quality
Ma, Denghao; Chen, Yueguo; Chang, Kevin Chen Chuan; Du, Xiaoyong. (2018). "[[Leveraging Fine-Grained Wikipedia Categories for Entity Search]]". International World Wide Web Conferences Steering Committee. DOI: 10.1145/3178876.3186074.
English Wikipedia
{{cite journal |last1=Ma |first1=Denghao |last2=Chen |first2=Yueguo |last3=Chang |first3=Kevin Chen Chuan |last4=Du |first4=Xiaoyong |title=Leveraging Fine-Grained Wikipedia Categories for Entity Search |date=2018 |doi=10.1145/3178876.3186074 |url=https://wikipediaquality.com/wiki/Leveraging_Fine-Grained_Wikipedia_Categories_for_Entity_Search |journal=International World Wide Web Conferences Steering Committee}}
HTML
Ma, Denghao; Chen, Yueguo; Chang, Kevin Chen Chuan; Du, Xiaoyong. (2018). "<a href="https://wikipediaquality.com/wiki/Leveraging_Fine-Grained_Wikipedia_Categories_for_Entity_Search">Leveraging Fine-Grained Wikipedia Categories for Entity Search</a>". International World Wide Web Conferences Steering Committee. DOI: 10.1145/3178876.3186074.