Czech Named Entity Corpus

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10372106" target="_blank" >RIV/00216208:11320/17:10372106 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Czech Named Entity Corpus

Popis výsledku v původním jazyce

We present a corpus of Czech sentences with manually annotated named entities, in which a rich two-level hierarchy of named entity types was used. The corpus was the first available large Czech named entity resource and since 2007, it has stimulated the research in this field for Czech. We describe the two-level fine-grained hierarchy allowing embedded entities and the motivations leading to its design. We further discuss the data selection and the annotation process. We then show how the data can be used for training a named entity recognizer and we perform a number of experiments to critically evaluate the impact of the decisions made in the process of annotation on the named entity recognizer performance. We thoroughly discuss the effect of sentence selection, corpus size, part-of-speech tagging and lemmatization, representativeness and bias of the named entity distribution, classification granularity and other corpus properties in terms of supervised machine learning.

Název v anglickém jazyce

Czech Named Entity Corpus

Popis výsledku anglicky

We present a corpus of Czech sentences with manually annotated named entities, in which a rich two-level hierarchy of named entity types was used. The corpus was the first available large Czech named entity resource and since 2007, it has stimulated the research in this field for Czech. We describe the two-level fine-grained hierarchy allowing embedded entities and the motivations leading to its design. We further discuss the data selection and the annotation process. We then show how the data can be used for training a named entity recognizer and we perform a number of experiments to critically evaluate the impact of the decisions made in the process of annotation on the named entity recognizer performance. We thoroughly discuss the effect of sentence selection, corpus size, part-of-speech tagging and lemmatization, representativeness and bias of the named entity distribution, classification granularity and other corpus properties in terms of supervised machine learning.

Druh

O - Ostatní výsledky

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/LM2010013" target="_blank" >LM2010013: LINDAT-CLARIN: Institut pro analýzu, zpracování a distribuci lingvistických dat</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

2017

Kód důvěrnosti údajů

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