A Wikipedia based Hybrid Ranking Method for Taxonomic Relation Extraction

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A Wikipedia based Hybrid Ranking Method for Taxonomic Relation Extraction
Authors
Xiaoshi Zhong
Publication date
2013
DOI
10.1007/978-3-642-45068-6_29
Links
Original

A Wikipedia based Hybrid Ranking Method for Taxonomic Relation Extraction - scientific work related to Wikipedia quality published in 2013, written by Xiaoshi Zhong.

Overview

This paper proposes a hybrid ranking method for taxonomic relation extraction (or select best position) in an existing taxonomy. This method is capable of effectively combining two resources, an existing taxonomy and Wikipedia, in order to select a most appropriate position for a term candidate in the existing taxonomy. Previous methods mainly focus on complex inference methods to select the best position among all the possible position in the taxonomy. In contrast, algorithm, a simple but effective one, leverage two kinds of information, the expression of and the ranking information of a term candidate, to select the best position for the term candidate (the hypernym of the term candidate in the existing taxonomy). Authors conduct approach on the agricultural domain and the experimental result indicates that the performances are significantly improved.

Embed

Wikipedia Quality

Zhong, Xiaoshi. (2013). "[[A Wikipedia based Hybrid Ranking Method for Taxonomic Relation Extraction]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-45068-6_29.

English Wikipedia

{{cite journal |last1=Zhong |first1=Xiaoshi |title=A Wikipedia based Hybrid Ranking Method for Taxonomic Relation Extraction |date=2013 |doi=10.1007/978-3-642-45068-6_29 |url=https://wikipediaquality.com/wiki/A_Wikipedia_based_Hybrid_Ranking_Method_for_Taxonomic_Relation_Extraction |journal=Springer, Berlin, Heidelberg}}

HTML

Zhong, Xiaoshi. (2013). &quot;<a href="https://wikipediaquality.com/wiki/A_Wikipedia_based_Hybrid_Ranking_Method_for_Taxonomic_Relation_Extraction">A Wikipedia based Hybrid Ranking Method for Taxonomic Relation Extraction</a>&quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-45068-6_29.