Abstract
Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we proposed a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject–object relations using a forward object decoder. Then, it finds 1-to-n subject–object relations using a backward subject decoder. Our experiments confirmed that the proposed model outperformed previous models, with an F1-score of 80.8% for the ACE (automatic content extraction) 2005 corpus and an F1-score of 78.3% for the NYT (New York Times) corpus.
Highlights
Relation extraction is a task that involves recognizing semantic relations among entities in a sentence [1]
With the significant success of deep neural networks in the field of natural language processing, many researchers have proposed various relation extraction models based on convolutional neural networks (CNNs)
Relation extraction models based on recurrent neural network (RNNs) have been proposed, including the long-short term memory (LSTM) model based on the dependency tree [7] and the LSTM model using the position-aware attention technique [8]
Summary
Relation extraction is a task that involves recognizing semantic relations We propose a dual pointer network model to efficiently extract multiple relations from a sentence through forward scanning (i.e., scanning from the first word to the last) and backward scanning (i.e., scanning from the last word to the first). The proposed model discovers an object of the current subject during forward scanning. Through forward scanning, all normal type relations can be found. SEO type relations are only partially found because a subject should point to only one object in the pointer network architecture. To address this limitation, the proposed model performs backward scanning to identify the subject of the current object.
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