Joint Learning of Entity Semantics and Relation Pattern for Relation Extraction

Relation extraction is identifying the relationship of two given entities in the text. It is an important step in the task of knowledge extraction, which plays a vital role in automatic construction of knowledge base. When extracting entities' relations from sentences, some keywords can reflect the relation pattern, besides, the semantic properties of given entities can also help to distinguish some confusing relations. Based on the above observations, we propose a mixture convolutional neural network for the task of relation extraction, which can simultaneously learn the semantic properties of entities and the keyword information related to the relation. We conduct experiments on the SemEval-2010 Task 8 dataset. The method we propose achieves the state-of-the-art result without using any external information. Additionally, the experimental results also show that our approach can learn the semantic relationship of the given entities effectively.

[1]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[2]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[3]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[4]  Wei Chu,et al.  Multi-category Classification by Soft-Max Combination of Binary Classifiers , 2003, Multiple Classifier Systems.

[5]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[6]  Nanda Kambhatla,et al.  Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Information Extraction , 2004, ACL.

[7]  Sanda M. Harabagiu,et al.  UTD: Classifying Semantic Relations by Combining Lexical and Semantic Resources , 2010, *SEMEVAL.

[8]  Zhi Jin,et al.  Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths , 2015, EMNLP.

[9]  Jiawei Han,et al.  Document clustering using locality preserving indexing , 2005, IEEE Transactions on Knowledge and Data Engineering.

[10]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[11]  eon BottouAT.  Stochastic Gradient Learning in Neural Networks , 2022 .

[12]  Dongyan Zhao,et al.  Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling , 2015, EMNLP.

[13]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[14]  Mo Yu.  Factor-based Compositional Embedding Models , 2014 .

[15]  Xianpei Han,et al.  A Feature-Enriched Tree Kernel for Relation Extraction , 2014, ACL.

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

[17]  Peng Wang,et al.  Short Text Clustering via Convolutional Neural Networks , 2015, VS@HLT-NAACL.

[18]  Edward Y. Chang,et al.  Parallel Spectral Clustering in Distributed Systems , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[20]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

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

[22]  Wanxiang Che,et al.  Learning Semantic Hierarchies via Word Embeddings , 2014, ACL.

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