Abstract
Joint Self-Attention Based Neural Networks for Semantic Relation Extraction
Highlights
Relation extraction is a fundamental task in information extraction, which has important applications in question answering, information retrieval, big data analysis etc
We use self-attention mechanism to get semantic representation of text segment that is related to every entity, which can attention the sentence itself to extract relevant information and capture Long-distance dependence on semantics to capture the interaction between semantics of two entities in a sentence we propose joint self-attention bi-long short-term memory (LSTM)(SA-BiLSTM) to model the internal structure of the sentence to obtain the importance of each word in the sentence without relying on additional information
(2) To capture long distance dependencies of semantics, and the interaction between semantics of two entities we propose joint self-attention bi-LSTM(SA-Bi-LSTM) to model the internal structure of the sentence to obtain the importance of each word with the sentence without relying on additional information
Summary
Relation extraction is a fundamental task in information extraction, which has important applications in question answering, information retrieval, big data analysis etc. Traditional approaches to relation extraction take entity recognition as a predecessor step in the pipeline predicting relations between given entities. There has been a surge of interest in relation extraction task. The traditional methods are mainly based on supervised relation extraction [Suchanek, Ifrim and Weikum (2006); Qian, Zhou, Kong et al (2008)], which usually suffer from the issue that lacks sufficient labelled relation-specific training data. Artificial feature extraction methods need some tools of natural language processing, which lead to the propagation of the errors in the existing tools and hinders the performance of some systems [Bach and Badaskar (2007)]
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