.png){kind=icon}
.png){kind=icon}
Return
Direction-sensitive relation extraction using Bi-SDP attention model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2020.105928.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Relation extraction is a crucial task of natural language processing (NLP). It plays a key role in question answering, web search, and information retrieval and so on. Previous research on this task has verified the effectiveness of using attention mechanisms, shortest dependency paths (SDP) and LSTM. However, most of these methods focus on learning a semantic representation of the whole sentence, highlighting the importance of partial words, or pruning the sentence with SDP. They ignore the lose of information in these methods, such as the dependency relation of each word and preposition words to indicate the relation direction. Besides, the SDP-based approach is prone to over-pruning. Based on the above observations, this paper presents a framework with a Bi-directional SDP (Bi-SDP) attention mechanism to tackle these challenges. The Bi-SDP is a novel representation of SDP, including original SDP and its reverse. The attention mechanism, based on Bi-SDP, builds a parallel word-level attention to capture relational semantic words and directional words. Furthermore, we explored a novel pruning strategy to minimize the length of input instance and the number of RNN cells simultaneously. Moreover, experiments are conducted on two datasets: SemEval-2010 Task 8 dataset and KBP37 dataset. Compared with the previous public models, our method can achieve better competitive performance on the SemEval-2010 Task 8 dataset and outperform existing models on the KBP37 dataset. Additionally, our experimental results also evidence that the directional prepositions in sentences are useful for relation extraction and can improve the performance of relationship with apparent physical direction. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Relation extraction
Shortest dependency path
Recurrent neural network
Self-attention
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W
Organization
No organization information available