Word Sense Clustering and Clusterability.
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| Title: | Word Sense Clustering and Clusterability. |
|---|---|
| Authors: | McCarthy, Diana1 diana@dianamccarthy.co.uk, Apidianaki, Marianna2 marianna.apidianaki@limsi.fr, Erk, Katrin3 katrin.erk@mail.utexas.edu |
| Source: | Computational Linguistics. Jun2016, Vol. 42 Issue 2, p245-275. 31p. |
| Subjects: | Terms & phrases, Sublanguage, Linguistics, Idiolect, Machine learning |
| Abstract: | Word sense disambiguation and the related field of automated word sense induction traditionally assume that the occurrences of a lemma can be partitioned into senses. But this seems to be a much easier task for some lemmas than others. Our work builds on recent work that proposes describing word meaning in a graded fashion rather than through a strict partition into senses; in this article we argue that not all lemmas may need the more complex graded analysis, depending on their partitionability. Although there is plenty of evidence from previous studies and from the linguistics literature that there is a spectrum of partitionability of word meanings, this is the first attempt to measure the phenomenon and to couple the machine learning literature on clusterability with word usage data used in computational linguistics. We propose to operationalize partitionability as clusterability, a measure of how easy the occurrences of a lemma are to cluster. We test two ways of measuring clusterability: (1) existing measures from the machine learning literature that aim to measure the goodness of optimal k-means clusterings, and (2) the idea that if a lemma is more clusterable, two clusterings based on two different "views" of the same data points will be more congruent. The two views that we use are two different sets of manually constructed lexical substitutes for the target lemma, on the one hand monolingual paraphrases, and on the other hand translations. We apply automatic clustering to the manual annotations. We use manual annotations because we want the representations of the instances that we cluster to be as informative and "clean" as possible. We show that when we control for polysemy, our measures of clusterability tend to correlate with partitionability, in particular some of the type-(1) clusterability measures, and that these measures outperform a baseline that relies on the amount of overlap in a soft clustering. [ABSTRACT FROM AUTHOR] |
| Copyright of Computational Linguistics is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 116185546 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Word Sense Clustering and Clusterability. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22McCarthy%2C+Diana%22">McCarthy, Diana</searchLink><relatesTo>1</relatesTo><i> diana@dianamccarthy.co.uk</i><br /><searchLink fieldCode="AR" term="%22Apidianaki%2C+Marianna%22">Apidianaki, Marianna</searchLink><relatesTo>2</relatesTo><i> marianna.apidianaki@limsi.fr</i><br /><searchLink fieldCode="AR" term="%22Erk%2C+Katrin%22">Erk, Katrin</searchLink><relatesTo>3</relatesTo><i> katrin.erk@mail.utexas.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink>. Jun2016, Vol. 42 Issue 2, p245-275. 31p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Terms+%26+phrases%22">Terms & phrases</searchLink><br /><searchLink fieldCode="DE" term="%22Sublanguage%22">Sublanguage</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistics%22">Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Idiolect%22">Idiolect</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Word sense disambiguation and the related field of automated word sense induction traditionally assume that the occurrences of a lemma can be partitioned into senses. But this seems to be a much easier task for some lemmas than others. Our work builds on recent work that proposes describing word meaning in a graded fashion rather than through a strict partition into senses; in this article we argue that not all lemmas may need the more complex graded analysis, depending on their partitionability. Although there is plenty of evidence from previous studies and from the linguistics literature that there is a spectrum of partitionability of word meanings, this is the first attempt to measure the phenomenon and to couple the machine learning literature on clusterability with word usage data used in computational linguistics. We propose to operationalize partitionability as clusterability, a measure of how easy the occurrences of a lemma are to cluster. We test two ways of measuring clusterability: (1) existing measures from the machine learning literature that aim to measure the goodness of optimal k-means clusterings, and (2) the idea that if a lemma is more clusterable, two clusterings based on two different "views" of the same data points will be more congruent. The two views that we use are two different sets of manually constructed lexical substitutes for the target lemma, on the one hand monolingual paraphrases, and on the other hand translations. We apply automatic clustering to the manual annotations. We use manual annotations because we want the representations of the instances that we cluster to be as informative and "clean" as possible. We show that when we control for polysemy, our measures of clusterability tend to correlate with partitionability, in particular some of the type-(1) clusterability measures, and that these measures outperform a baseline that relies on the amount of overlap in a soft clustering. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computational Linguistics is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/COLI_a_00247 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 245 Subjects: – SubjectFull: Terms & phrases Type: general – SubjectFull: Sublanguage Type: general – SubjectFull: Linguistics Type: general – SubjectFull: Idiolect Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Word Sense Clustering and Clusterability. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: McCarthy, Diana – PersonEntity: Name: NameFull: Apidianaki, Marianna – PersonEntity: Name: NameFull: Erk, Katrin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 08912017 Numbering: – Type: volume Value: 42 – Type: issue Value: 2 Titles: – TitleFull: Computational Linguistics Type: main |
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