The nature of statistical learning theory
The nature of statistical learning theory
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Automatic corpus-based Thai word extraction with the c4.5 learning algorithm
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Accessor variety criteria for Chinese word extraction
Computational Linguistics
A measure of term representativeness based on the number of co-occurring salient words
COLING '02 Proceedings of the 19th international 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
Two-character Chinese word extraction based on hybrid of internal and contextual measures
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
The use of SVM for chinese new word identification
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Chinese term extraction using different types of relevance
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Incremental Chinese lexicon extraction with minimal resources on a domain-specific corpus
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Chinese terminology extraction using EM-Based transfer learning method
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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This paper presents a new approach for term extraction using minimal resources. A term candidate extraction algorithm is proposed to identify features of the relatively stable and domain independent term delimiters rather than that of the terms. For term verification, a link analysis based method is proposed to calculate the relevance between term candidates and the sentences in the domain specific corpus from which the candidates are extracted. The proposed approach requires no prior domain knowledge, no general corpora, no full segmentation and minimal adaptation for new domains. Consequently, the method can be used in any domain corpus and it is especially useful for resource-limited domains. Evaluations conducted on two different domains for Chinese term extraction show quite significant improvements over existing techniques and also verify the efficiency and relative domain independent nature of the approach. Experiments on new term extraction also indicate that the approach is quite effective for identifying new terms in a domain making it useful for domain knowledge update.