TitleClassifying relations via long short term memory networks along shortest dependency paths
AuthorsXu, Yan
Mou, Lili
Li, Ge
Chen, Yunchuan
Peng, Hao
Jin, Zhi
AffiliationSoftware Institute, Peking University, China
University of Chinese Academy of Sciences, China
Issue Date2015
PublisherConference on Empirical Methods in Natural Language Processing, EMNLP 2015
CitationConference on Empirical Methods in Natural Language Processing, EMNLP 2015.Lisbon, Portugal,2015/1/1.
AbstractRelation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an F1-score of 83.7%, higher than competing methods in the literature. ? 2015 Association for Computational Linguistics.
Appears in Collections:软件与微电子学院

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