Sequence Labeling for Constituent Parsing: A Comparative Study and Encoding Innovations.
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| Title: | Sequence Labeling for Constituent Parsing: A Comparative Study and Encoding Innovations. |
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| Authors: | Roca, Diego1 (AUTHOR) d.roca1@udc.es, Vilares, David1 (AUTHOR) david.vilares@udc.es, Gómez-Rodríguez, Carlos1 (AUTHOR) carlos.gomez@udc.es |
| Source: | Computational Linguistics. Jun2026, Vol. 52 Issue 2, p495-539. 45p. |
| Subjects: | Encoding, Parsing (Grammar), Artificial neural networks, Computational linguistics, Natural language processing, Evaluation methodology |
| Abstract: | Various encodings have been proposed to cast constituent parsing in terms of a sequence labeling task. However, unlike in the case of dependency parsing, existing comparisons have not been entirely homogeneous and, to the best of our knowledge, there is no systematic evaluation of these encodings under uniform configurations. A homogeneous evaluation needs to account for various aspects that could influence results, either by controlling for these aspects to ensure uniformity (e.g., network architecture, parameter settings, postprocessing of ill-formed output), or by systematically analyzing their impact (e.g., the impact of binary versus arbitrary structures). In this article, we: (1) compare different encodings comprehensively both theoretically and empirically, on a modern neural architecture and across nine languages, and (2) introduce new encodings and variants, including an encoding that our analysis finds particularly accurate and compact. [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: 194945042 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sequence Labeling for Constituent Parsing: A Comparative Study and Encoding Innovations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Roca%2C+Diego%22">Roca, Diego</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> d.roca1@udc.es</i><br /><searchLink fieldCode="AR" term="%22Vilares%2C+David%22">Vilares, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> david.vilares@udc.es</i><br /><searchLink fieldCode="AR" term="%22Gómez-Rodríguez%2C+Carlos%22">Gómez-Rodríguez, Carlos</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> carlos.gomez@udc.es</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink>. Jun2026, Vol. 52 Issue 2, p495-539. 45p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Encoding%22">Encoding</searchLink><br /><searchLink fieldCode="DE" term="%22Parsing+%28Grammar%29%22">Parsing (Grammar)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+linguistics%22">Computational linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Various encodings have been proposed to cast constituent parsing in terms of a sequence labeling task. However, unlike in the case of dependency parsing, existing comparisons have not been entirely homogeneous and, to the best of our knowledge, there is no systematic evaluation of these encodings under uniform configurations. A homogeneous evaluation needs to account for various aspects that could influence results, either by controlling for these aspects to ensure uniformity (e.g., network architecture, parameter settings, postprocessing of ill-formed output), or by systematically analyzing their impact (e.g., the impact of binary versus arbitrary structures). In this article, we: (1) compare different encodings comprehensively both theoretically and empirically, on a modern neural architecture and across nine languages, and (2) introduce new encodings and variants, including an encoding that our analysis finds particularly accurate and compact. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194945042 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/COLI.a.603 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 45 StartPage: 495 Subjects: – SubjectFull: Encoding Type: general – SubjectFull: Parsing (Grammar) Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Computational linguistics Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Evaluation methodology Type: general Titles: – TitleFull: Sequence Labeling for Constituent Parsing: A Comparative Study and Encoding Innovations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Roca, Diego – PersonEntity: Name: NameFull: Vilares, David – PersonEntity: Name: NameFull: Gómez-Rodríguez, Carlos IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08912017 Numbering: – Type: volume Value: 52 – Type: issue Value: 2 Titles: – TitleFull: Computational Linguistics Type: main |
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