Difference between revisions of "Leveraging Fine-Grained Wikipedia Categories for Entity Search"
(wikilinks) |
(+ infobox) |
||
Line 1: | Line 1: | ||
+ | {{Infobox work | ||
+ | | title = Leveraging Fine-Grained Wikipedia Categories for Entity Search | ||
+ | | date = 2018 | ||
+ | | authors = [[Denghao Ma]]<br />[[Yueguo Chen]]<br />[[Kevin Chen Chuan Chang]]<br />[[Xiaoyong Du]] | ||
+ | | doi = 10.1145/3178876.3186074 | ||
+ | | link = https://dl.acm.org/citation.cfm?doid=3178876.3186074 | ||
+ | }} | ||
'''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]]. | '''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 == | == 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. |
Revision as of 07:04, 19 February 2021
Authors | Denghao Ma Yueguo Chen Kevin Chen Chuan Chang Xiaoyong Du |
---|---|
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.