Relation extraction based on CNN and Bi-LSTM
Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extractio...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2018-09-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2018074 |
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| _version_ | 1841530128190930944 |
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| author | Xiaobin ZHANG Fucai CHEN Ruiyang HUANG |
| author_facet | Xiaobin ZHANG Fucai CHEN Ruiyang HUANG |
| author_sort | Xiaobin ZHANG |
| collection | DOAJ |
| description | Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extraction methods apply convolutional neural network (CNN) and long short-term memory neural network (LSTM) methods.However,CNN just considers the correlation between consecutive words and ignores the correlation between discontinuous words.On the other side,although LSTM takes correlation between long-distance words into account,the extraction features are not sufficiently extracted.In order to solve these problems,a relation extraction method that combining CNN and LSTM was proposed.three methods were used to carry out the experiments,and confirmed the effectiveness of these methods,which had some improvement in F1 score. |
| format | Article |
| id | doaj-art-530f8e59fb9c42a6b8e4534cb30c79f3 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2018-09-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-530f8e59fb9c42a6b8e4534cb30c79f32025-01-15T03:12:59ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2018-09-014445159554194Relation extraction based on CNN and Bi-LSTMXiaobin ZHANGFucai CHENRuiyang HUANGRelation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extraction methods apply convolutional neural network (CNN) and long short-term memory neural network (LSTM) methods.However,CNN just considers the correlation between consecutive words and ignores the correlation between discontinuous words.On the other side,although LSTM takes correlation between long-distance words into account,the extraction features are not sufficiently extracted.In order to solve these problems,a relation extraction method that combining CNN and LSTM was proposed.three methods were used to carry out the experiments,and confirmed the effectiveness of these methods,which had some improvement in F1 score.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2018074relation extractionconvolution neural networkslong short-term memoryattention mechanism |
| spellingShingle | Xiaobin ZHANG Fucai CHEN Ruiyang HUANG Relation extraction based on CNN and Bi-LSTM 网络与信息安全学报 relation extraction convolution neural networks long short-term memory attention mechanism |
| title | Relation extraction based on CNN and Bi-LSTM |
| title_full | Relation extraction based on CNN and Bi-LSTM |
| title_fullStr | Relation extraction based on CNN and Bi-LSTM |
| title_full_unstemmed | Relation extraction based on CNN and Bi-LSTM |
| title_short | Relation extraction based on CNN and Bi-LSTM |
| title_sort | relation extraction based on cnn and bi lstm |
| topic | relation extraction convolution neural networks long short-term memory attention mechanism |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2018074 |
| work_keys_str_mv | AT xiaobinzhang relationextractionbasedoncnnandbilstm AT fucaichen relationextractionbasedoncnnandbilstm AT ruiyanghuang relationextractionbasedoncnnandbilstm |