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Attention-Based LSTM with Filter Mechanism for Entity Relation Classification
| Content Provider | MDPI |
|---|---|
| Author | Jin, Yanliang Wu, Dijia Guo, Weisi |
| Copyright Year | 2020 |
| Description | Relation classification is an important research area in the field of natural language processing (NLP), which aims to recognize the relationship between two tagged entities in a sentence. The noise caused by irrelevant words and the word distance between the tagged entities may affect the relation classification accuracy. In this paper, we present a novel model multi-head attention long short term memory (LSTM) network with filter mechanism (MALNet) to extract the text features and classify the relation of two entities in a sentence. In particular, we combine LSTM with attention mechanism to obtain the shallow local information and introduce a filter layer based on attention mechanism to strength the available information. Besides, we design a semantic rule for marking the key word between the target words and construct a key word layer to extract its semantic information. We evaluated the performance of our model on SemEval-2010 Task8 dataset and KBP-37 dataset. We achieved an F1-score of 86.3% on SemEval-2010 Task8 dataset and F1-score of 61.4% on KBP-37 dataset, which shows that our method is superior to the previous state-of-the-art methods. |
| Starting Page | 1729 |
| e-ISSN | 20738994 |
| DOI | 10.3390/sym12101729 |
| Journal | Symmetry |
| Issue Number | 10 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-10-19 |
| Access Restriction | Open |
| Subject Keyword | Symmetry Cybernetical Science Relation Classification Attention Mechanism Bidirectional Lstm Network Natural Language Processing |
| Content Type | Text |
| Resource Type | Article |
