A Semantics-Aware Head-Driven Approach for Multilingual Dependency Parsing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3ARYEULSIT" target="_blank" >RIV/00216208:11320/26:RYEULSIT - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3601225" target="_blank" >http://dx.doi.org/10.1109/ACCESS.2025.3601225</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3601225" target="_blank" >10.1109/ACCESS.2025.3601225</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Semantics-Aware Head-Driven Approach for Multilingual Dependency Parsing

Popis výsledku v původním jazyce

Dependency parsing is essential for language modeling because it offers a structured understanding of the syntactic relationships between words in a sentence. While recent advancements in large language models have greatly advanced the field of natural language processing, dependency parsing remains highly relevant for several key reasons. This paper introduces an effective method for improving dependency parsing which is based on a semantics-aware token embedding model. We propose to incorporate the ConceptNet embeddings which are trained by a retrofitting algorithm into a bidirectional recurrent neural network. The new model outperforms a strong baseline that employs a state-of-the-art method across three dependency treebanks, covering both low-resource and high-resource languages—Indonesian, Vietnamese, and English—achieving an improvement of approximately 1.21% in labeled attachment score. We also show that this method outperforms the popular transformer-based BERT model in capturing syntactic dependency between tokens. The new parser together with all trained models are made available under an open-source license, facilitating community engagement and advancement of natural language processing research for two low-resource languages with around 300 million users worldwide. © 2013 IEEE.

Název v anglickém jazyce

A Semantics-Aware Head-Driven Approach for Multilingual Dependency Parsing

Popis výsledku anglicky

Dependency parsing is essential for language modeling because it offers a structured understanding of the syntactic relationships between words in a sentence. While recent advancements in large language models have greatly advanced the field of natural language processing, dependency parsing remains highly relevant for several key reasons. This paper introduces an effective method for improving dependency parsing which is based on a semantics-aware token embedding model. We propose to incorporate the ConceptNet embeddings which are trained by a retrofitting algorithm into a bidirectional recurrent neural network. The new model outperforms a strong baseline that employs a state-of-the-art method across three dependency treebanks, covering both low-resource and high-resource languages—Indonesian, Vietnamese, and English—achieving an improvement of approximately 1.21% in labeled attachment score. We also show that this method outperforms the popular transformer-based BERT model in capturing syntactic dependency between tokens. The new parser together with all trained models are made available under an open-source license, facilitating community engagement and advancement of natural language processing research for two low-resource languages with around 300 million users worldwide. © 2013 IEEE.

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

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

148456-148466

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105013990333