Named entity recognition for Hungarian using various machine learning algorithms

In this paper we introduce a statistical Named Entity recognizer (NER) system for the Hungarian language. We examined three methods for identifying and disambiguating proper nouns (Artificial Neural Network, Support Vector Machine, C4.5 Decision Tree), their combinations and the effects of dimension...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Farkas Richárd
Szarvas György
Kocsor András
Testületi szerző: Conference on Hungarian Computational Linguistics (2.) (2004) (Szeged)
Dokumentumtípus: Cikk
Megjelent: 2006
Sorozat:Acta cybernetica 17 No. 3
Kulcsszavak:Számítástechnika, Nyelvészet - számítógép alkalmazása
Tárgyszavak:
Online Access:http://acta.bibl.u-szeged.hu/12787
Leíró adatok
Tartalmi kivonat:In this paper we introduce a statistical Named Entity recognizer (NER) system for the Hungarian language. We examined three methods for identifying and disambiguating proper nouns (Artificial Neural Network, Support Vector Machine, C4.5 Decision Tree), their combinations and the effects of dimensionality reduction as well. We used a segment of Szeged Corpus [5] for training and validation purposes, which consists of short business news articles collected from MTI (Hungarian News Agency, www.mti.hu). Our results were presented at the Second Conference on Hungarian Computational Linguistics [7]. Our system makes use of both language dependent features (describing the orthography of proper nouns in Hungarian) and other, language independent information such as capitalization. Since we avoided the inclusion of large gazetteers of pre-classified entities, the system remains portable across languages without requiring any major modification, as long as the few specialized orthographical and syntactic characteristics are collected for a new target language. The best performing model achieved an F measure accuracy of 91.95%.
Terjedelem/Fizikai jellemzők:633-646
ISSN:0324-721X