Investigating the cross-lingual translatability of VerbNet-style classification.
Saved in:
| Title: | Investigating the cross-lingual translatability of VerbNet-style classification. |
|---|---|
| Authors: | Majewska, Olga1, Vulić, Ivan1, McCarthy, Diana1, Huang, Yan1, Murakami, Akira1, Korhonen, Anna1, Laippala, Veronika2 |
| Source: | Language Resources & Evaluation. Sep2018, Vol. 52 Issue 3, p771-799. 29p. |
| Subjects: | Lexicon, Multilingual computing, Study & teaching of verbs, General semantics, Semantics |
| Abstract: | VerbNet—the most extensive online verb lexicon currently available for English—has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper. [ABSTRACT FROM AUTHOR] |
| Copyright of Language Resources & Evaluation is the property of Springer Nature 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 131216684 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Investigating the cross-lingual translatability of VerbNet-style classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Majewska%2C+Olga%22">Majewska, Olga</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Vulić%2C+Ivan%22">Vulić, Ivan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22McCarthy%2C+Diana%22">McCarthy, Diana</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Huang%2C+Yan%22">Huang, Yan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Murakami%2C+Akira%22">Murakami, Akira</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Korhonen%2C+Anna%22">Korhonen, Anna</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Laippala%2C+Veronika%22">Laippala, Veronika</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Language+Resources+%26+Evaluation%22">Language Resources & Evaluation</searchLink>. Sep2018, Vol. 52 Issue 3, p771-799. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Lexicon%22">Lexicon</searchLink><br /><searchLink fieldCode="DE" term="%22Multilingual+computing%22">Multilingual computing</searchLink><br /><searchLink fieldCode="DE" term="%22Study+%26+teaching+of+verbs%22">Study & teaching of verbs</searchLink><br /><searchLink fieldCode="DE" term="%22General+semantics%22">General semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: VerbNet—the most extensive online verb lexicon currently available for English—has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Language Resources & Evaluation is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=131216684 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10579-017-9403-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 771 Subjects: – SubjectFull: Lexicon Type: general – SubjectFull: Multilingual computing Type: general – SubjectFull: Study & teaching of verbs Type: general – SubjectFull: General semantics Type: general – SubjectFull: Semantics Type: general Titles: – TitleFull: Investigating the cross-lingual translatability of VerbNet-style classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Majewska, Olga – PersonEntity: Name: NameFull: Vulić, Ivan – PersonEntity: Name: NameFull: McCarthy, Diana – PersonEntity: Name: NameFull: Huang, Yan – PersonEntity: Name: NameFull: Murakami, Akira – PersonEntity: Name: NameFull: Korhonen, Anna – PersonEntity: Name: NameFull: Laippala, Veronika IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 1574020X Numbering: – Type: volume Value: 52 – Type: issue Value: 3 Titles: – TitleFull: Language Resources & Evaluation Type: main |
| ResultId | 1 |
