摘要: Relational extraction plays an important role in the field of natural language processing to predict semantic
relationships between entities in a sentence. Currently, most models have typically utilized the natural
language processing tools to capture high-level features with an attention mechanism to mitigate the adverse
effects of noise in sentences for the prediction results. However, in the task of relational classification, these
attention mechanisms do not take full advantage of the semantic information of some keywords which have
information on relational expressions in the sentences. Therefore, we propose a novel relation extraction model
based on the attention mechanism with keywords, named Relation Extraction Based on Keywords Attention (REKA).
In particular, the proposed model makes use of bi-directional GRU (Bi-GRU) to reduce computation, obtain the
representation of sentences , and extracts prior knowledge of entity pair without any NLP tools. Besides the calculation
of the entity-pair similarity, Keywords attention in the REKA model also utilizes a linear-chain conditional random
field (CRF) combining entity-pair features, similarity features between entity-pair features, and its hidden
vectors, to obtain the attention weight resulting from the marginal distribution of each word. Experiments
demonstrate that the proposed approach can utilize keywords incorporating relational expression semantics
in sentences without the assistance of any high-level features and achieve better performance than traditional
methods.
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期刊:
DATA INTELLIGENCE
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分类:
计算机科学
>>
计算机科学的集成理论
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引用:
ChinaXiv:202211.00421
(或此版本
ChinaXiv:202211.00421V1)
DOI:10.1162/dint_a_00147
CSTR:32003.36.ChinaXiv.202211.00421.V1
- 推荐引用方式:
Yuanyuan, Zhang,Yu, Chen, Shengkang, Yu, Xiaoqin, Gu,Mengqiong, Song,Yu, Peng,Jianxia, Chen,Qi, Liu.(2022).Bi-GRU Relation Extraction Model Based on Keywords Attention.DATA INTELLIGENCE.doi:10.1162/dint_a_00147
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