Combining Grammatical and Relational Approaches. A Hybrid Method for the Identification of Candidate Collocations from Corpora

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AALQBLMBB" target="_blank" >RIV/00216208:11320/25:ALQBLMBB - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2024.mwe-1.18" target="_blank" >https://aclanthology.org/2024.mwe-1.18</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Combining Grammatical and Relational Approaches. A Hybrid Method for the Identification of Candidate Collocations from Corpora

Popis výsledku v původním jazyce

We present an evaluation of three different methods for the automatic identification of candidate collocations in corpora, part of a research project focused on the development of a learner dictionary of Italian collocations. We compare the commonly used POS-based method and the syntactic dependency-based method with a hybrid method integrating both approaches. We conduct a statistical analysis on a sample corpus of written and spoken texts of different registers. Results show that the hybrid method can correctly detect more candidate collocations against a human annotated benchmark. The scores are particularly high in adjectival modifier rela- tions. A hybrid approach to candidate collocation identification seems to lead to an improvement in the quality of results.

Název v anglickém jazyce

Combining Grammatical and Relational Approaches. A Hybrid Method for the Identification of Candidate Collocations from Corpora

Popis výsledku anglicky

We present an evaluation of three different methods for the automatic identification of candidate collocations in corpora, part of a research project focused on the development of a learner dictionary of Italian collocations. We compare the commonly used POS-based method and the syntactic dependency-based method with a hybrid method integrating both approaches. We conduct a statistical analysis on a sample corpus of written and spoken texts of different registers. Results show that the hybrid method can correctly detect more candidate collocations against a human annotated benchmark. The scores are particularly high in adjectival modifier rela- tions. A hybrid approach to candidate collocation identification seems to lead to an improvement in the quality of results.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024

ISBN

978-2-493-81420-3

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

138-146

Název nakladatele

ELRA and ICCL

Místo vydání

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Místo konání akce

Torino, Italia

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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