Difference between revisions of "The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet"
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− | '''The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet''' - scientific work related to Wikipedia quality published in 2011, written by Elisabeth Niemann and Iryna Gurevych. | + | {{Infobox work |
+ | | title = The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet | ||
+ | | date = 2011 | ||
+ | | authors = [[Elisabeth Niemann]]<br />[[Iryna Gurevych]] | ||
+ | | link = https://dl.acm.org/citation.cfm?id=2002691 | ||
+ | }} | ||
+ | '''The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Elisabeth Niemann]] and [[Iryna Gurevych]]. | ||
== Overview == | == Overview == | ||
− | Authors propose a method to automatically align WordNet synsets and Wikipedia articles to obtain a sense inventory of higher coverage and quality. For each WordNet synset, authors first extract a set of Wikipedia articles as alignment candidates; in a second step, authors determine which article (if any) is a valid alignment, i.e. is about the same sense or concept. In this paper, authors go significantly beyond state-of-the-art word overlap approaches, and apply a threshold-based Personalized PageRank method for the disambiguation step. Authors show that WordNet synsets can be aligned to Wikipedia articles with a performance of up to 0.78 F1-Measure based on a comprehensive, well-balanced reference dataset consisting of 1,815 manually annotated sense alignment candidates. The fully-aligned resource as well as the reference dataset is publicly available. | + | Authors propose a method to automatically align [[WordNet]] synsets and [[Wikipedia]] articles to obtain a sense inventory of higher coverage and quality. For each WordNet synset, authors first extract a set of Wikipedia articles as alignment candidates; in a second step, authors determine which article (if any) is a valid alignment, i.e. is about the same sense or concept. In this paper, authors go significantly beyond state-of-the-art word overlap approaches, and apply a threshold-based Personalized PageRank method for the disambiguation step. Authors show that WordNet synsets can be aligned to Wikipedia articles with a performance of up to 0.78 F1-Measure based on a comprehensive, well-balanced reference dataset consisting of 1,815 manually annotated sense alignment candidates. The fully-aligned resource as well as the reference dataset is publicly available. |
+ | |||
+ | == Embed == | ||
+ | === Wikipedia Quality === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Niemann, Elisabeth; Gurevych, Iryna. (2011). "[[The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet]]". Association for Computational Linguistics. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === English Wikipedia === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | {{cite journal |last1=Niemann |first1=Elisabeth |last2=Gurevych |first2=Iryna |title=The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet |date=2011 |url=https://wikipediaquality.com/wiki/The_People's_Web_Meets_Linguistic_Knowledge:_Automatic_Sense_Alignment_of_Wikipedia_and_Wordnet |journal=Association for Computational Linguistics}} | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === HTML === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Niemann, Elisabeth; Gurevych, Iryna. (2011). &quot;<a href="https://wikipediaquality.com/wiki/The_People's_Web_Meets_Linguistic_Knowledge:_Automatic_Sense_Alignment_of_Wikipedia_and_Wordnet">The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet</a>&quot;. Association for Computational Linguistics. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | |||
+ | |||
+ | [[Category:Scientific works]] |
Latest revision as of 05:40, 29 January 2021
Authors | Elisabeth Niemann Iryna Gurevych |
---|---|
Publication date | 2011 |
Links | Original |
The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet - scientific work related to Wikipedia quality published in 2011, written by Elisabeth Niemann and Iryna Gurevych.
Overview
Authors propose a method to automatically align WordNet synsets and Wikipedia articles to obtain a sense inventory of higher coverage and quality. For each WordNet synset, authors first extract a set of Wikipedia articles as alignment candidates; in a second step, authors determine which article (if any) is a valid alignment, i.e. is about the same sense or concept. In this paper, authors go significantly beyond state-of-the-art word overlap approaches, and apply a threshold-based Personalized PageRank method for the disambiguation step. Authors show that WordNet synsets can be aligned to Wikipedia articles with a performance of up to 0.78 F1-Measure based on a comprehensive, well-balanced reference dataset consisting of 1,815 manually annotated sense alignment candidates. The fully-aligned resource as well as the reference dataset is publicly available.
Embed
Wikipedia Quality
Niemann, Elisabeth; Gurevych, Iryna. (2011). "[[The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet]]". Association for Computational Linguistics.
English Wikipedia
{{cite journal |last1=Niemann |first1=Elisabeth |last2=Gurevych |first2=Iryna |title=The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet |date=2011 |url=https://wikipediaquality.com/wiki/The_People's_Web_Meets_Linguistic_Knowledge:_Automatic_Sense_Alignment_of_Wikipedia_and_Wordnet |journal=Association for Computational Linguistics}}
HTML
Niemann, Elisabeth; Gurevych, Iryna. (2011). "<a href="https://wikipediaquality.com/wiki/The_People's_Web_Meets_Linguistic_Knowledge:_Automatic_Sense_Alignment_of_Wikipedia_and_Wordnet">The People's Web Meets Linguistic Knowledge: Automatic Sense Alignment of Wikipedia and Wordnet</a>". Association for Computational Linguistics.