Improving Neural Relation Extraction with Implicit Mutual Relations

Abstract

Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing methods predict the relation between an entity pair by learning the relation from the training sentences, which contain the targeted entity pair. In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible. After constructing an entity proximity graph based on the implicit mutual relations, we preserve the semantic relations of entity pairs via embedding each vertex of the graph into a low-dimensional space. As a result, we can easily and flexibly integrate the implicit mutual relations and other entity information, such as entity types, into the existing RE methods. Our experimental results on a New York Times and another Google Distant Supervision datasets suggest that our proposed neural RE framework provides a promising improvement for the RE task, and significantly outperforms the state-of-the-art methods. Moreover, the component for mining implicit mutual relations is so flexible that can help to improve the performance of both CNN-based and RNN-based RE models significant.

12 pages

References in corpus (11)

• Distributed Representations of Words and Phrases and their Compositionality
• Explaining and Harnessing Adversarial Examples
• LINE: Large-scale Information Network Embedding
• Geometric deep learning: going beyond Euclidean data
• Recurrent Models of Visual Attention
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Reinforcement Learning for Relation Classification from Noisy Data
• Classifying Relations by Ranking with Convolutional Neural Networks
• Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
• Neural Collective Entity Linking
• Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction

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