High-order neural network in entity relationship extraction
In this paper, a kind of high-order neural network is proposed to extract entity relations in natural language. In this kind of network, different parameters absorb non-overlapping information from separated data respectively, which makes parameters more significant for understanding. This neural ne...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201818903025 |
| Summary: | In this paper, a kind of high-order neural network is proposed to extract entity relations in natural language. In this kind of network, different parameters absorb non-overlapping information from separated data respectively, which makes parameters more significant for understanding. This neural network can alleviate overfitting problem in some degree. When solving the task of entity relationship extraction, this network can give a result no worse than current methods. |
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| ISSN: | 2261-236X |