دورية أكاديمية
Attention-Based LSTM with Filter Mechanism for Entity Relation Classification
| العنوان: | Attention-Based LSTM with Filter Mechanism for Entity Relation Classification |
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| المؤلفون: | Yanliang Jin, Dijia Wu, Weisi Guo |
| المصدر: | Symmetry, Vol 12, Iss 10, p 1729 (2020) |
| بيانات النشر: | MDPI AG, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Mathematics |
| مصطلحات موضوعية: | relation classification, attention mechanism, bidirectional LSTM network, natural language processing, Mathematics, QA1-939 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 12101729 2073-8994 |
| Relation: | https://www.mdpi.com/2073-8994/12/10/1729; https://doaj.org/toc/2073-8994 |
| DOI: | 10.3390/sym12101729 |
| URL الوصول: | https://doaj.org/article/e315fae38e2d4daa86bd8a9dfc46d3a5 |
| رقم الأكسشن: | edsdoj.315fae38e2d4daa86bd8a9dfc46d3a5 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 12101729 20738994 |
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| DOI: | 10.3390/sym12101729 |