Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.
%0 Journal Article
%1 Karkaletsis:1999:NRG:595358.595565
%A Karkaletsis, Vangelis
%A Paliouras, Georgios
%A Petasis, Georgios
%A Manousopoulou, Natasa
%A Spyropoulos, Constantine D.
%C Hingham, MA, USA
%D 1999
%I Kluwer Academic Publishers
%J Journal of Intelligent and Robotic Systems
%K extraction, information learning, machine named-entity recognition
%N 2
%P 123--135
%R 10.1023/A:1008124406923
%T Named-Entity Recognition from Greek and English Texts
%U http://www.ellogon.org/petasis/bibliography/JIRS1999/JIRS-1999.pdf
%V 26
%X Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.
@article{Karkaletsis:1999:NRG:595358.595565,
abstract = {Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.},
added-at = {2011-08-10T12:37:26.000+0200},
address = {Hingham, MA, USA},
author = {Karkaletsis, Vangelis and Paliouras, Georgios and Petasis, Georgios and Manousopoulou, Natasa and Spyropoulos, Constantine D.},
biburl = {https://www.bibsonomy.org/bibtex/214f28b2124bc790c1f74662021e3ae7a/petasis},
doi = {10.1023/A:1008124406923},
interhash = {ced67fe2ff36f78129fcd9ca8086661c},
intrahash = {14f28b2124bc790c1f74662021e3ae7a},
issn = {0921-0296},
journal = {Journal of Intelligent and Robotic Systems},
keywords = {extraction, information learning, machine named-entity recognition},
month = {October},
number = 2,
pages = {123--135},
publisher = {Kluwer Academic Publishers},
timestamp = {2011-08-10T12:37:27.000+0200},
title = {{N}amed-{E}ntity {R}ecognition from {G}reek and {E}nglish {T}exts},
url = {http://www.ellogon.org/petasis/bibliography/JIRS1999/JIRS-1999.pdf},
volume = 26,
year = 1999
}