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Relation Classification via Convolutional Deep Neural Network

Relation Classification via Convolutional Deep Neural Network Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou and Jun Zhao National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences 95 Zhongguancun East Road, Beijing 100190, China Abstract The state-of-the-art methods used for Relation Classification are primarily based on statistical ma- chine learning, and their performance strongly depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing natural language process- ing (NLP) systems, which leads to the propagation of the errors in the existing tools and hinders the performance of these systems.

In this paper, we propose a convolutional DNN to extract lexical and sentence level features for relation classication; our method effectively alleviates the shortcomings of traditional features. 3 Methodology 3.1 The Neural Network Architecture network takes an input sentence and discovers multiple levels of feature extraction, where higher levels

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