Transfer Learning for Czech Historical Named Entity Recognition

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Transfer Learning for Czech Historical Named Entity Recognition

Popis výsledku v původním jazyce

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 This is particularly evident in the historical document processing field.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.

Název v anglickém jazyce

Transfer Learning for Czech Historical Named Entity Recognition

Popis výsledku anglicky

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 This is particularly evident in the historical document processing field.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.

Druh

D - Stať ve sborníku

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

<a href="/cs/project/EF17_048%2F0007267" target="_blank" >EF17_048/0007267: VaV inteligentních komponent pokročilých technologií pro plzeňskou metropolitní oblast</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Deep Learning for Natural Language Processing Methods and Applications

ISBN

978-954-452-072-4

ISSN

1313-8502

e-ISSN

—

Počet stran výsledku

7

Strana od-do

576-582

Název nakladatele

INCOMA, Ltd.

Místo vydání

Shoumen

Místo konání akce

Shoumen, Bulgaria

Datum konání akce

1. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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