A multilingual named entity recognition system using boosting and C4.5 decision tree learning algorithms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4265 LNAI, Issue , 2006, Pages 267-278

  Szarvas, György ^a   Farkas, Richard ^a   Kocsor, András ^a  

^a UNIVERSITY OF SZEGED   (Hungary)

Author keywords

Boosting; C4.5; Decision tree; Machine learning; Named entity recognition; NER; Voting

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; STATISTICAL METHODS; TREES (MATHEMATICS);

BOOSTING; C4.5; DECISION TREE; NAMED ENTITY RECOGNITION (NER); VOTING;

ALGORITHMS;

EID: 33750685685     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11893318_27     Document Type: Conference Paper

References (16)

1. Daniel M. Bikel, Richard L. Schwanz, and Ralph M. Weischedel: An algorithm that learns what's in a name. Machine Learning, 34-1-3 (1999) 211-231
2. Xavier Carreras, Llufs Marques and Lluís Padró: Named Entity Extraction using AdaBoost In: Proceedings of CoNLL-2002, Taipei, Taiwan, (2002) 167-170
3. Hai L. Chieu and Hwee T. Ng.: Named Entity Recognition with a Maximum Entropy Approach. Proceedings of CoNLL-2003 (2003) 160-163
4. Nancy Chinchor.: MUC-7 Named Entity Task Definition, in Proceedings of Seventh Message Understanding Conference (1998)
5. Silviu Cucerzan and Daniel Yarowsky: Language-independent named entity recognition combining morphological and contextual evidence. Proceedings of Joint SIGDAT Conf. on EMNLP/VLC (1999)
6. Dóra Csendes, János Csirik and Tibor Gyimóthy: The Szeged Corpus: A POS tagged and Syntactically Annotated Hungarian Natural Language Corpus. Proceedings of TSD 2004, vol. 3206(2004)41-49.
7. Farkas Richárd, Szarvas György, Kocsor András: Named Entity Recognition for Hungarian using various Machine Learning Algorithms accepted for publication in Acta Cybernetica (http://www.inf.u-szeged.hu/ ~rfarkas/ACT A2006_hun_namedentity.pdf)
8. Radu Florian, Abe Ittycheriah, Hongyan Jing and Tong Zhang: Named Entity Recognition through Classifier Combination. Proceedings of CoNLL-2003 (.2003) 168-171.
9. Kata Gábor, Eniko Héja, Agnes Mészáros, Bálint Sass: Nyílt tokenosztályok reprezentá ciójának technológiája. IKTA-00037/2002, Budapest, Hungary (2002)
10. Jin-Dong Kim, Tomoko Ohta, Yoshimasa Tsuruoka, Yuka Tateisi and Nigel Collier: Introduction to the Bio-Entity Task at JNLPBA. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (2004)
11. Ross Quinlan: C4.5: Programs for machine learning, Morgan Kaufmann (1993)
12. Gábor Prószéky: Syntax As Meta-Morphology. Proceedings of COLING-96, Vol.2 (1996) 1123-1126
13. Rob E. Shapire: The Strength of Weak Learnability. Machine Learnings, Vol. 5 (1990) 197-227
14. György Szarvas, Richard Farkas, László Felföldi, András Kocsor, János Csirik: A highly accurate Named Entity corpus for Hungarian, Proceedings of International Conference on Language Resources and Evaluation (2006)
15. Erik F. Tjong Kim Sang, and Fien De Meulder: Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition, Proceedings of CoNLL-2003 (2003)
16. Ian H. Witten and Eibe Frank: Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco (2005)