DSREFC: Improving Distantly-supervised Neural Relation Extraction Using Feature Combination
Authors: 
Jibin He, Yawei Zhao, Gang LuoAuthors Info & Claims
Jibin He, Yawei Zhao, Gang LuoAuthors Info & ClaimsPages 524 - 529
Abstract
Distant supervisory relationship extraction can automatically align the expected entity pairs, and automatically obtain a large number of annotation data, thus saving a lot of labor costs. However, the automatic acquisition of annotated data will lead to the introduction of noise data, making little effect of relation extraction task. To solve this problem, we propose the relation extraction model DSREFC, which integrates semantic features and syntactic features into the representation and uses attention mechanism to obtain bag representation. The DSREFC model has three characteristics: 1) The BERT+Bi-LSTM is used as the text representation extractor to extract the semantic information of the text. 2) the grammatical information is extracted with the GCN network used in the text, and combine theBi-LSTM output with the GCN output to obtain a distributed representation of each token. 3) The two-step attention mechanism is used to remove the influence of the noise data by giving the noise data a lower weight value. Attention is used to obtain the sentence representation for each token and attention is used to obtain the packet representation for each sentence in the packet. Experiments show that the DSREFC model combining semantic features and grammatical features can significantly improve the effect of relation extraction.
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Index Terms
- DSREFC: Improving Distantly-supervised Neural Relation Extraction Using Feature Combination
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February 2020
607 pages
ISBN:9781450376426
DOI:10.1145/3383972
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Published: 26 May 2020
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ICMLC 2020
ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing
February 15 - 17, 2020
Shenzhen, China
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