Using Language Models to Improve Rule-based Linguistic Annotation of Modern Historical Japanese Corpora

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3ASZHJDYMU" target="_blank" >RIV/00216208:11320/22:SZHJDYMU - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2022.latechclfl-1.5" target="_blank" >https://aclanthology.org/2022.latechclfl-1.5</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Using Language Models to Improve Rule-based Linguistic Annotation of Modern Historical Japanese Corpora

Original language description

Annotation of unlabeled textual corpora with linguistic metadata is a fundamental technology in many scholarly workflows in the digital humanities (DH). Pretrained natural language processing pipelines offer tokenization, tagging, and dependency parsing of raw text simultaneously using an annotation scheme like Universal Dependencies (UD). However, the accuracy of these UD tools remains unknown for historical texts and current methods lack mechanisms that enable helpful evaluations by domain experts. To address both points for the case of Modern Historical Japanese text, this paper proposes the use of unsupervised domain adaptation methods to develop a domain-adapted language model (LM) that can flag instances of inaccurate UD output from a pretrained LM and the use of these instances to form rules that, when applied, improves pretrained annotation accuracy. To test the efficacy of the proposed approach, the paper evaluates the domain-adapted LM against three baselines that are not adapted to the historical domain. The experiments conducted demonstrate that the domain-adapted LM improves UD annotation in the Modern Historical Japanese domain and that rules produced using this LM are best indicative of characteristics of the domain in terms of out-of-vocabulary rate and candidate normalized form discovery for “difficult” bigram terms.

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

2022

Confidentiality

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

Article name in the collection

Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

ISBN

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ISSN

2951-2093

e-ISSN

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Number of pages

10

Pages from-to

30-39

Publisher name

International Conference on Computational Linguistics

Place of publication

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Event location

Gyeongju, Republic of Korea

Event date

Jan 1, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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