Distantly Supervised Neural Network Model for Relation Extraction

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• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9427
• 期：1
• 页码：253-266
• 全文大小：1,339 KB
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• 作者单位：Zhen Wang (17)
  Baobao Chang (17)
  Zhifang Sui (17)
  
  17. Key Laboratory of Computational Linguistics, Ministry of Education School of Electronics Engineering and Computer Science, Peking University Collaborative Innovation Center for Language Ability, Xuzhou, 221009, China
  
• 丛书名：Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
• ISBN：978-3-319-25816-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base (KB) with free texts. Albeit easy to scale to thousands of different relations, this procedure suffers from introducing wrong labels because the relations in knowledge base may not be expressed by aligned sentences (mentions). In this paper, we propose a novel approach to alleviate the problem of distant supervision with representation learning in the framework of deep neural network. Our model - Distantly Supervised Neural Network (DSNN) - constructs the more powerful mention level representation by tensor-based transformation and further learns the entity pair level representation which aggregates and denoises the features of associated mentions. With this denoised representation, all of the relation labels can be jointly learned. Experimental results show that with minimal feature engineering, our model generally outperforms state-of-the-art methods for distantly supervised relation extraction.