Sentence Identification with BOS and EOS Label Combinations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AEI8A59QL" target="_blank" >RIV/00216208:11320/23:EI8A59QL - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159857166&partnerID=40&md5=e5801dc21246ea677d1221b817ffadf0" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159857166&partnerID=40&md5=e5801dc21246ea677d1221b817ffadf0</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Sentence Identification with BOS and EOS Label Combinations

Original language description

"The sentence is a fundamental unit in many NLP applications. Sentence segmentation is widely used as the first preprocessing task, where an input text is split into consecutive sentences considering the end of the sentence (EOS) as their boundaries. This task formulation relies on a strong assumption that the input text consists only of sentences, or what we call the sentential units (SUs). However, real-world texts often contain non-sentential units (NSUs) such as metadata, sentence fragments, nonlinguistic markers, etc. which are unreasonable or undesirable to be treated as a part of an SU. To tackle this issue, we formulate a novel task of sentence identification, where the goal is to identify SUs while excluding NSUs in a given text. To conduct sentence identification, we propose a simple yet effective method which combines the beginning of the sentence (BOS) and EOS labels to determine the most probable SUs and NSUs based on dynamic programming. To evaluate this task, we design an automatic, language-independent procedure to convert the Universal Dependencies corpora into sentence identification benchmarks. Finally, our experiments on the sentence identification task demonstrate that our proposed method generally outperforms sentence segmentation baselines which only utilize EOS labels. © 2023 Association for Computational Linguistics."

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

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Publication year

2023

Confidentiality

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

Article name in the collection

"EACL - Conf. Eur. Chapter Assoc. Comput. Linguist., Find. EACL"

ISBN

978-195942947-0

ISSN

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e-ISSN

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Number of pages

16

Pages from-to

343-358

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

Washington DC, USA

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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