CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks
Yang HT(杨海彤); Zong, Chengqing
Source PublicationTransactions of the Association for Computational Linguistics
2015
Issue3Pages:271-282
AbstractIn current systems for syntactic and semantic dependency parsing, people usually define a very high-dimensional feature space to achieve good performance. But these systems often suffer severe performance drops on outof-domain test data due to the diversity of features of different domains. This paper focuses on how to relieve this domain adaptation problem with the help of unlabeled target domain data. We propose a deep learning method to adapt both syntactic and semantic parsers. With additional unlabeled target domain data, our method can learn a latent feature representation (LFR) that is beneficial to both domains. Experiments on English data in the CoNLL 2009 shared task show that our method largely reduced the performance drop on out-of-domain test data. Moreover, we get a Macro F1 score that is 2.32 points higher than the best system in the CoNLL 2009 shared task in out-of-domain tests.
KeywordDeep Belief Networks
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11857
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorZong, Chengqing
Recommended Citation
GB/T 7714
Yang HT,Zong, Chengqing. Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks[J]. Transactions of the Association for Computational Linguistics,2015(3):271-282.
APA Yang HT,&Zong, Chengqing.(2015).Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks.Transactions of the Association for Computational Linguistics(3),271-282.
MLA Yang HT,et al."Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks".Transactions of the Association for Computational Linguistics .3(2015):271-282.
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