Valence extraction using EM selection and co-occurrence matrices.
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| Title: | Valence extraction using EM selection and co-occurrence matrices. |
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| Authors: | Dębowski, Łukasz1,2 debowski@cwi.nl |
| Source: | Language Resources & Evaluation. Dec2009, Vol. 43 Issue 4, p301-327. 27p. 4 Charts. |
| Subjects: | Verbs, Deep structure (Linguistics), Grammar, Algorithms, Pattern recognition systems |
| Abstract: | This paper discusses two new procedures for extracting verb valences from raw texts, with an application to the Polish language. The first novel technique, the EM selection algorithm, performs unsupervised disambiguation of valence frame forests, obtained by applying a non-probabilistic deep grammar parser and some post-processing to the text. The second new idea concerns filtering of incorrect frames detected in the parsed text and is motivated by an observation that verbs which take similar arguments tend to have similar frames. This phenomenon is described in terms of newly introduced co-occurrence matrices. Using co-occurrence matrices, we split filtering into two steps. The list of valid arguments is first determined for each verb, whereas the pattern according to which the arguments are combined into frames is computed in the following stage. Our best extracted dictionary reaches an F-score of 45%, compared to an F-score of 39% for the standard frame-based BHT filtering. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 45284385 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Valence extraction using EM selection and co-occurrence matrices. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dębowski%2C+Łukasz%22">Dębowski, Łukasz</searchLink><relatesTo>1,2</relatesTo><i> debowski@cwi.nl</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Language+Resources+%26+Evaluation%22">Language Resources & Evaluation</searchLink>. Dec2009, Vol. 43 Issue 4, p301-327. 27p. 4 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Verbs%22">Verbs</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+structure+%28Linguistics%29%22">Deep structure (Linguistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Grammar%22">Grammar</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper discusses two new procedures for extracting verb valences from raw texts, with an application to the Polish language. The first novel technique, the EM selection algorithm, performs unsupervised disambiguation of valence frame forests, obtained by applying a non-probabilistic deep grammar parser and some post-processing to the text. The second new idea concerns filtering of incorrect frames detected in the parsed text and is motivated by an observation that verbs which take similar arguments tend to have similar frames. This phenomenon is described in terms of newly introduced co-occurrence matrices. Using co-occurrence matrices, we split filtering into two steps. The list of valid arguments is first determined for each verb, whereas the pattern according to which the arguments are combined into frames is computed in the following stage. Our best extracted dictionary reaches an F-score of 45%, compared to an F-score of 39% for the standard frame-based BHT filtering. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10579-009-9100-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 301 Subjects: – SubjectFull: Verbs Type: general – SubjectFull: Deep structure (Linguistics) Type: general – SubjectFull: Grammar Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Pattern recognition systems Type: general Titles: – TitleFull: Valence extraction using EM selection and co-occurrence matrices. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dębowski, Łukasz IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 1574020X Numbering: – Type: volume Value: 43 – Type: issue Value: 4 Titles: – TitleFull: Language Resources & Evaluation Type: main |
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