Word association norms, mutual information, and lexicography
Computational Linguistics
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Termight: identifying and translating technical terminology
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
A corpus-based approach to automatic compound extraction
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Extracting nested collocations
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
A simple but powerful automatic term extraction method
COMPUTERM '02 COLING-02 on COMPUTERM 2002: second international workshop on computational terminology - Volume 14
A nonparametric method for extraction of candidate phrasal terms
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Paradigmatic modifiability statistics for the extraction of complex multi-word terms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Chinese term extraction using minimal resources
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
| Hi-index | 0.00 |
This paper presents an automatic Chinese multi-word term extraction method based on the integration of Web information and term component. We extract candidate terms by identifying delimiters, and filter invalid terms by checking the context terms in the Google result pages that are returned by Google when the candidate term is set as search request. Term component is taken into account to estimate the termhood. Inspired by the economical law of term generating, we propose two measures of a candidate term to be a true term: the first measure is based on domain speciality of term, and the second one is based on the similarity between a candidate and a template that contains structured information of terms. Experiments on IT domain and Medicine domain show that our method is effective and portable in different domains.