A Two-channel model for relation extraction using multiple trained word embeddings

Wang，Yinmiao1; Han，Zhimin1; You，Keyou2; Lin，Zhiyun3,4

2022-11-14

10.1016/j.knosys.2022.109701

KNOWLEDGE-BASED SYSTEMS   Impact Factor & Quartile

0950-7051

1872-7409

As an essential task in the field of knowledge graph, relation extraction (RE) has received extensive attention from researchers. Since the existing RE methods only adopt one trained word embedding to obtain sentence representation, the polysemy problem cannot be well solved. In order to alleviate the polysemy in RE, this paper proposes a Two-channel model by adopting multiple trained word embeddings, in which one channel is a bidirectional long-short-term memory network based on an attention mechanism (Bi-LSTM-ATT), and the other channel is a convolutional neural network (CNN). Furthermore, a two-channel fusion method is proposed based on this model to deal with polysemy problem in RE. As a result, the Two-channel model achieves 85.42% and 62.2% F1-scores on the Semeval-2010 Task 8 dataset and KBP37 dataset, respectively. The experiment results show that the Two-channel model performs better than most existing models under the condition without using the external features generated by natural language processing (NLP) tools. On the other hand, the two-channel fusion method also obtains a better performance than either concatenation or addition on the two channels.

Relation extraction Trained word embedding Two-channel model

SCI ; EI

English

Others

National Key Research and Development Program of China[2018AAA0101601];

Computer Science

Computer Science, Artificial Intelligence

WOS:000860573400010

ELSEVIER

20223612683863

Convolutional neural networks ; Extraction ; Knowledge graph ; Long short-term memory ; Natural language processing systems

Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Chemical Operations:802.3

COMPUTER SCIENCE

2-s2.0-85137066301

Scopus

1.Artificial Intelligence Institute,School of Automation,Hangzhou Dianzi University,Hangzhou,China
2.Department of Automation,and BNRist,Tsinghua University,Beijing,China
3.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,China
4.Peng Cheng Laboratory,Shenzhen,China

Wang，Yinmiao,Han，Zhimin,You，Keyou,et al. A Two-channel model for relation extraction using multiple trained word embeddings[J]. KNOWLEDGE-BASED SYSTEMS,2022,255.

Wang，Yinmiao,Han，Zhimin,You，Keyou,&Lin，Zhiyun.(2022).A Two-channel model for relation extraction using multiple trained word embeddings.KNOWLEDGE-BASED SYSTEMS,255.

Wang，Yinmiao,et al."A Two-channel model for relation extraction using multiple trained word embeddings".KNOWLEDGE-BASED SYSTEMS 255(2022).