Joint Self-Attention Based Neural Networks for Semantic Relation Extraction

Abstract

Relation extraction is an important task in NLP community. However, some models often fail in capturing Long-distance dependence on semantics, and the interaction between semantics of two entities is ignored. In this paper, we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM (SA-Bi-LSTM) to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information, and capture Long-distance dependence on semantics. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrated that the proposed method is effective against relation classification, which can obtain state-ofthe-art classification accuracy just with minimal feature engineering.

Attention-Based LSTM with Filter Mechanism for Entity Relation Classification

“…In recent years, attention mechanism used to learn text classification [35], question answering [36], and named entity recognition [37]. The words in the sentence contain different levels of importance [38]. To effectively distinguish the valid features and the invalid, we do some fine-tuning of attention mechanisms to build an attention filter layer.…”