Transfer Learning for Czech Historical Named Entity Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43963747" target="_blank" >RIV/49777513:23520/21:43963747 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2021.ranlp-main.65.pdf" target="_blank" >https://aclanthology.org/2021.ranlp-main.65.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.26615/978-954-452-072-4_065" target="_blank" >10.26615/978-954-452-072-4_065</a>

Result language

angličtina

Original language name

Transfer Learning for Czech Historical Named Entity Recognition

Original language description

Nowadays, named entity recognition (NER) achieved excellent results on the standard corpora. However, big issues are emerging with a need for an application in a specific domain, because it requires a suitable annotated corpus with adapted NE tag-set. This is particularly evident in the historical document processing field. The main goal of this paper consists of proposing and evaluation of several transfer learning methods to increase the score of the Czech historical NER. We study several information sources, and we use two neural nets for NE modeling and recognition. We employ two corpora for evaluation of our transfer learning methods, namely Czech named entity corpus and Czech historical named entity corpus. We show that BERT representation with fine-tuning and only the simple classifier trained on the union of corpora achieves excellent results.

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

<a href="/en/project/EF17_048%2F0007267" target="_blank" >EF17_048/0007267: Research and Development of Intelligent Components of Advanced Technologies for the Pilsen Metropolitan Area (InteCom)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Article name in the collection

Deep Learning for Natural Language Processing Methods and Applications

ISBN

978-954-452-072-4

ISSN

1313-8502

e-ISSN

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Number of pages

7

Pages from-to

576-582

Publisher name

INCOMA, Ltd.

Place of publication

Shoumen

Event location

Shoumen, Bulgaria

Event date

Sep 1, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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