Li, Yuming;
Ni, Pin;
Li, Gangmin;
Wang, Xutao;
Dai, Zhenjin;
(2020)
Inter-Personal Relation Extraction Model Based on Bidirectional GRU and Attention Mechanism.
In:
2019 IEEE 5th International Conference on Computer and Communications (ICCC).
(pp. pp. 1867-1871).
IEEE: Chengdu, China.
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Abstract
Inter-Personal Relationship Extraction is an important part of knowledge extraction and is also the fundamental work of constructing the knowledge graph of people's relationships. Compared with the traditional pattern recognition methods, the deep learning methods are more prominent in the relation extraction (RE) tasks. At present, the research of Chinese relation extraction technology is mainly based on the method of kernel function and Distant Supervision. In this paper, we propose a Chinese relation extraction model based on Bidirectional GRU network and Attention mechanism. Combining with the structural characteristics of the Chinese language, the input vector is input in the form of word vectors. Aiming at the problem of context memory, a Bidirectional GRU neural network is used to fuse the input vectors. The feature information of the word level is extracted from a sentence, and the sentence feature is extracted through the Attention mechanism of the word level. To verify the feasibility of this method, we use the distant supervision method to extract data from websites and compare it with existing relationship extraction methods. The experimental results show that Bi-directional GRU with Attention mechanism model can make full use of all the feature information of sentences, and the accuracy of Bi-directional GRU model is significantly higher than that of other neural network models without Attention mechanism.
| Type: | Proceedings paper |
|---|---|
| Title: | Inter-Personal Relation Extraction Model Based on Bidirectional GRU and Attention Mechanism |
| Event: | IEEE 5th International Conference on Computer and Communications (ICCC) |
| Dates: | 6 Dec 2019 - 9 Dec 2019 |
| ISBN-13: | 978-1-7281-4743-7 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICCC47050.2019.9064050 |
| Publisher version: | https://doi.org/10.1109/ICCC47050.2019.9064050 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Feature extraction; Data mining; Training; Logic gates; Neural networks; Task analysis; Semantics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159892 |
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