Named Entity Linking in English-Czech Parallel Corpus

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00137343" target="_blank" >RIV/00216224:14330/24:00137343 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/book/10.1007/978-3-031-70563-2" target="_blank" >https://link.springer.com/book/10.1007/978-3-031-70563-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-70563-2_12" target="_blank" >10.1007/978-3-031-70563-2_12</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Named Entity Linking in English-Czech Parallel Corpus

Popis výsledku v původním jazyce

We present a procedure to build relatively quickly new resources with annotated named entities and their linking to Wikidata. First, we applied state-of-the-art models for named entity recognition on a sentence-aligned parallel English-Czech corpus. We selected the most common entity classes: person, location, organization, and miscellaneous. Second, we manually checked the corpus in a suitably set annotation application. Third, we used a state-of-the-art tool for named entity linking and enhanced the ranking using sentence embeddings obtained by sentence transformers. We then checked manually whether the linking to knowledge bases was correct. As a result, we added two annotation layers to an existing parallel corpus: one with the named entities and one with links to Wikidata. The corpus contains 14,881 parallel Czech-English sentences and 3,769 links to Wikidata. The corpus can be used for training more robust named entity recognition and named entity linking models and for linguistic research of parallel news texts.

Název v anglickém jazyce

Named Entity Linking in English-Czech Parallel Corpus

Popis výsledku anglicky

We present a procedure to build relatively quickly new resources with annotated named entities and their linking to Wikidata. First, we applied state-of-the-art models for named entity recognition on a sentence-aligned parallel English-Czech corpus. We selected the most common entity classes: person, location, organization, and miscellaneous. Second, we manually checked the corpus in a suitably set annotation application. Third, we used a state-of-the-art tool for named entity linking and enhanced the ranking using sentence embeddings obtained by sentence transformers. We then checked manually whether the linking to knowledge bases was correct. As a result, we added two annotation layers to an existing parallel corpus: one with the named entities and one with links to Wikidata. The corpus contains 14,881 parallel Czech-English sentences and 3,769 links to Wikidata. The corpus can be used for training more robust named entity recognition and named entity linking models and for linguistic research of parallel news texts.

Druh

D - Stať ve sborníku

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

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Text, Speech, and Dialogue : 27th International Conference, TSD 2024, Brno, Czech Republic, September 9–13, 2024, Proceedings, Part I

ISBN

9783031705625

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

147-158

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Brno

Datum konání akce

9. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001307840300012