Annotation Projection-based Dependency Parser Development for Nepali

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AYCYJ96EI" target="_blank" >RIV/00216208:11320/23:YCYJ96EI - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/summary/13f0cd58-cefa-451c-b019-593f55164d36-bb92386a/relevance/1" target="_blank" >https://www.webofscience.com/wos/woscc/summary/13f0cd58-cefa-451c-b019-593f55164d36-bb92386a/relevance/1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3542696" target="_blank" >10.1145/3542696</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Annotation Projection-based Dependency Parser Development for Nepali

Popis výsledku v původním jazyce

"Building computational resources and tools for the under-resourced languages is strenuous for any Natural Language Processing task. This article presents the first dependency parser for an under-resourced Indian language, Nepali. A prerequisite for developing a parser for a language is a corpus annotated with the desired linguistic representations known as a treebank. With an aim of cross-lingual learning and typological research, we use a Bengali treebank to build a Bengali-Nepali parallel corpus and apply the method of annotation projection from the Bengali treebank to build a treebank for Nepali. With the developed treebank, MaltParser (with all algorithms for projective dependency structures) and a Neural network-based parser have been used to build Nepali parser models. The Neural network-based parser produced state-of-the-art results with 81.2 Unlabeled Attachment Score, 73.2 Label Accuracy, and 66.1 Labeled Attachment Score on the gold test data. The parser models have also been evaluated with the predicted Part-of-speech (POS)-tagged test data. A statistical POS tagger using Conditional Random Field has been developed for predicting the POS tags of the test data."

Název v anglickém jazyce

Annotation Projection-based Dependency Parser Development for Nepali

Popis výsledku anglicky

"Building computational resources and tools for the under-resourced languages is strenuous for any Natural Language Processing task. This article presents the first dependency parser for an under-resourced Indian language, Nepali. A prerequisite for developing a parser for a language is a corpus annotated with the desired linguistic representations known as a treebank. With an aim of cross-lingual learning and typological research, we use a Bengali treebank to build a Bengali-Nepali parallel corpus and apply the method of annotation projection from the Bengali treebank to build a treebank for Nepali. With the developed treebank, MaltParser (with all algorithms for projective dependency structures) and a Neural network-based parser have been used to build Nepali parser models. The Neural network-based parser produced state-of-the-art results with 81.2 Unlabeled Attachment Score, 73.2 Label Accuracy, and 66.1 Labeled Attachment Score on the gold test data. The parser models have also been evaluated with the predicted Part-of-speech (POS)-tagged test data. A statistical POS tagger using Conditional Random Field has been developed for predicting the POS tags of the test data."

Druh

J_ost - Ostatní články v recenzovaných periodicích

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

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

"ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING"

ISSN

2375-4699

e-ISSN

—

Svazek periodika

22

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

1-19

Kód UT WoS článku

000963394900005

EID výsledku v databázi Scopus

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