Quality of Word Vectors and Its Impact on Named Entity Recognition in Czech

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F20%3A43918937" target="_blank" >RIV/62156489:43110/20:43918937 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.11118/ejobsat.2020.010" target="_blank" >https://doi.org/10.11118/ejobsat.2020.010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/ejobsat.2020.010" target="_blank" >10.11118/ejobsat.2020.010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Quality of Word Vectors and Its Impact on Named Entity Recognition in Czech

Popis výsledku v původním jazyce

Named Entity Recognition (NER) focuses on finding named entities in text and classifying them into one of the entity types. Modern state-of-the-art NER approaches avoid using hand-crafted features and rely on feature-inferring neural network systems based on word embeddings. The paper analyzes the impact of different aspects related to word embeddings on the process and results of the named entity recognition task in Czech, which has not been investigated so far. Various aspects of word vectors preparation were experimentally examined to draw useful conclusions. The suitable settings in different steps were determined, including the used corpus, number of word vectors dimensions, used text preprocessing techniques, context window size, number of training epochs, and word vectors inferring algorithms and their specific parameters. The paper demonstrates that focusing on the process of word vectors preparation can bring a significant improvement for NER in Czech even without using additional language independent and dependent resources.

Název v anglickém jazyce

Quality of Word Vectors and Its Impact on Named Entity Recognition in Czech

Popis výsledku anglicky

Named Entity Recognition (NER) focuses on finding named entities in text and classifying them into one of the entity types. Modern state-of-the-art NER approaches avoid using hand-crafted features and rely on feature-inferring neural network systems based on word embeddings. The paper analyzes the impact of different aspects related to word embeddings on the process and results of the named entity recognition task in Czech, which has not been investigated so far. Various aspects of word vectors preparation were experimentally examined to draw useful conclusions. The suitable settings in different steps were determined, including the used corpus, number of word vectors dimensions, used text preprocessing techniques, context window size, number of training epochs, and word vectors inferring algorithms and their specific parameters. The paper demonstrates that focusing on the process of word vectors preparation can bring a significant improvement for NER in Czech even without using additional language independent and dependent resources.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

European Journal of Business Science and Technology

ISSN

2336-6494

e-ISSN

—

Svazek periodika

6

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

16

Strana od-do

154-169

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85099840520