Neural Relation Extraction with Multi-lingual Attention

Relation extraction has been widely used for finding unknown relational facts from plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the texts in various languages. To address this issue, we introduce a multi-lingual neural relation extraction framework, which employs mono-lingual attention to utilize the information within mono-lingual texts and further proposes cross-lingual attention to consider the information consistency and complementarity among cross-lingual texts. Experimental results on real-world datasets show that, our model can take advantage of multi-lingual texts and consistently achieve significant improvements on relation extraction as compared with baselines.

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  Shankar Kumar,et al.  Multilingual Open Relation Extraction Using Cross-lingual Projection , 2015, NAACL.

[3]  Andrew McCallum,et al.  Multilingual Relation Extraction using Compositional Universal Schema , 2015, NAACL.

[4]  Jun Zhao,et al.  Relation Classification via Convolutional Deep Neural Network , 2014, COLING.

[5]  Jun Zhao,et al.  Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks , 2015, EMNLP.

[6]  Daniel Jurafsky,et al.  Distant supervision for relation extraction without labeled data , 2009, ACL.

[7]  Florian Boudin,et al.  A Graph-based Approach to Cross-language Multi-document Summarization , 2011, Polibits.

[8]  Zhiyuan Liu,et al.  Neural Relation Extraction with Selective Attention over Instances , 2016, ACL.

[9]  Luke S. Zettlemoyer,et al.  Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations , 2011, ACL.

[10]  Mark Dredze,et al.  Improved Relation Extraction with Feature-Rich Compositional Embedding Models , 2015, EMNLP.

[11]  Andrew McCallum,et al.  Modeling Relations and Their Mentions without Labeled Text , 2010, ECML/PKDD.

[12]  Marie-Francine Moens,et al.  A machine learning approach to sentiment analysis in multilingual Web texts , 2009, Information Retrieval.

[13]  Andrew Y. Ng,et al.  Semantic Compositionality through Recursive Matrix-Vector Spaces , 2012, EMNLP.

[14]  Dmitry Zelenko,et al.  Kernel Methods for Relation Extraction , 2002, J. Mach. Learn. Res..

[15]  Ramesh Nallapati,et al.  Multi-instance Multi-label Learning for Relation Extraction , 2012, EMNLP.

[16]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[17]  Yang Liu,et al.  Query Lattice for Translation Retrieval , 2014, COLING.

[18]  Bowen Zhou,et al.  Classifying Relations by Ranking with Convolutional Neural Networks , 2015, ACL.

[19]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..