Yuhang Yang


2011

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Automatic Wrapper Generation and Maintenance
Yingju Xia | Yuhang Yang | Shu Zhang | Hao Yu
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

2010

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Fault-Tolerant Learning for Term Extraction
Yuhang Yang | Hao Yu | Yao Meng | Yingliang Lu | Yingju Xia
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

2009

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Chinese Term Extraction Using Different Types of Relevance
Yuhang Yang | Tiejun Zhao | Qin Lu | Dequan Zheng | Hao Yu
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2008

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Chinese Term Extraction Using Minimal Resources
Yuhang Yang | Qin Lu | Tiejun Zhao
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Chinese Term Extraction Based on Delimiters
Yuhang Yang | Qin Lu | Tiejun Zhao
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Existing techniques extract term candidates by looking for internal and contextual information associated with domain specific terms. The algorithms always face the dilemma that fewer features are not enough to distinguish terms from non-terms whereas more features lead to more conflicts among selected features. This paper presents a novel approach for term extraction based on delimiters which are much more stable and domain independent. The proposed approach is not as sensitive to term frequency as that of previous works. This approach has no strict limit or hard rules and thus they can deal with all kinds of terms. It also requires no prior domain knowledge and no additional training to adapt to new domains. Consequently, the proposed approach can be applied to different domains easily and it is especially useful for resource-limited domains. Evaluations conducted on two different domains for Chinese term extraction show significant improvements over existing techniques which verifies its efficiency and domain independent nature. Experiments on new term extraction indicate that the proposed approach can also serve as an effective tool for domain lexicon expansion.