Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
By Zhiquan Ye, Zhenhua Ling. 2019
140 citations recorded by the source corpus.
This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-noising by designing neural networks with intra-bag attentions. In this paper, both intra-bag and inter-bag attentions are considered in order to deal with the noise at sentence-level and bag-level respectively. First, relation-aware bag representations are calculated by weighting sentence embeddings using intra-bag attentions. Here, each possible relation is utilized as the query for attention calculation instead of only using the target relation in conventional methods. Furthermore, the representation of a group of bags in the training set which share the same relation label is calculated by weighting bag representations using a similarity-based inter-bag attention module. Finally, a bag group is utilized as a training sample when building our relation extractor. Experimental results on the New York Times dataset demonstrate the effectiveness of our proposed intra-bag and inter-bag attention modules. Our method also achieves better relation extraction accuracy than state-of-the-art methods on this dataset.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Christian Bauckhage
- Xindong Wu
- Hao Peng
- Lingfeng Zhong
- Qian Li
- Jia Wu
- Rajkumar Ramamurthy
- Jochen Garcke
Citations in the corpus
- Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, background): “This was originally done naı̈vely by considering each sentence that matches related entities in a graph as a training sample [80]; however, recently attention-based networks have been used to reduce the influence of noisy training samples [120].”
- A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “One example is Intra/Inter-Bag Attention [197].”