Real-world sentence boundary detection using multitask learning: A case study on French

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AFLLK296I" target="_blank" >RIV/00216208:11320/25:FLLK296I - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128531911&doi=10.1017%2fS1351324922000134&partnerID=40&md5=9be131708d834d63a090961e6a9c1911" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128531911&doi=10.1017%2fS1351324922000134&partnerID=40&md5=9be131708d834d63a090961e6a9c1911</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1017/S1351324922000134" target="_blank" >10.1017/S1351324922000134</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Real-world sentence boundary detection using multitask learning: A case study on French

Popis výsledku v původním jazyce

We propose a novel approach for sentence boundary detection in text datasets in which boundaries are not evident (e.g., sentence fragments). Although detecting sentence boundaries without punctuation marks has rarely been explored in written text, current real-world textual data suffer from widespread lack of proper start/stop signaling. Herein, we annotate a dataset with linguistic information, such as parts of speech and named entity labels, to boost the sentence boundary detection task. Via experiments, we obtained F1 scores up to 98.07% using the proposed multitask neural model, including a score of 89.41% for sentences completely lacking punctuation marks. We also present an ablation study and provide a detailed analysis to demonstrate the effectiveness of the proposed multitask learning method. © The Author(s), 2022. Published by Cambridge University Press.

Název v anglickém jazyce

Real-world sentence boundary detection using multitask learning: A case study on French

Popis výsledku anglicky

We propose a novel approach for sentence boundary detection in text datasets in which boundaries are not evident (e.g., sentence fragments). Although detecting sentence boundaries without punctuation marks has rarely been explored in written text, current real-world textual data suffer from widespread lack of proper start/stop signaling. Herein, we annotate a dataset with linguistic information, such as parts of speech and named entity labels, to boost the sentence boundary detection task. Via experiments, we obtained F1 scores up to 98.07% using the proposed multitask neural model, including a score of 89.41% for sentences completely lacking punctuation marks. We also present an ablation study and provide a detailed analysis to demonstrate the effectiveness of the proposed multitask learning method. © The Author(s), 2022. Published by Cambridge University Press.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Natural Language Engineering

ISSN

1351-3249

e-ISSN

—

Svazek periodika

30

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

150-170

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85128531911