An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion

Xiaolin Ma,Kaiqi Wu,Hailan Kuang,Xinhua Liu

doi:10.3390/app12031532

Abstract

Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. There is a novel thought that improvement is made on the entity relation extraction (ERE) by applying the dynamic context to the training. In reality, most existing models of joint extraction of entity and relation are based on static context, which always suffers from the feature missing issue, resulting in poor performance. To address the problem, we propose a span-based joint extraction method based on dynamic context and multi-feature fusion (SPERT-DC). The context area is picked dynamically with the help of threshold in feature selecting layer of the model. It is noted that we also use Bi-LSTM_ATT to improve compatibility of longer text in feature extracting layer and enhance context information by combining with the tags of entity in feature fusion layer. Furthermore, the model in this paper outperforms prior work by up to 1% F1 score on the public dataset, which has verified the efficiency of dynamic context on ERE model.

Highlights

Based on Dynamic ContextNowadays, accompaning a boom in the Internet, massive amounts of heterogenous information appear in our lives in the form of news artices, emails, blogs and Q&A
Entity relation extraction is an important branch of information extraction technology, the concrete task of which is to infer the semantic relationship between entity pairs from a given text corpus on the basis of semantic information
This paper proposes an entity relation extraction method based on dynamic context that can address the text context missing issue of existing models

Summary

Introduction

Method Based on Dynamic ContextNowadays, accompaning a boom in the Internet, massive amounts of heterogenous information appear in our lives in the form of news artices, emails, blogs and Q&A. How to effectively analyze the important data by information technology, help the related experts obtain the significant information, learn the risk level of hot events, and clarify whether the their occurrence can impact the national scientific and technological security has become an urgency suggesting being sovled quickly. Entity relation extraction is an important branch of information extraction technology, the concrete task of which is to infer the semantic relationship between entity pairs from a given text corpus on the basis of semantic information. A foundation for nature language processing, has been widely applied to information processing, automatic Q&A, automatic summarization and other fields, which has seen some initial achievements. With the rapid development of deep learning, researchers have applied neural networks to entity relation extraction tasks, which has brought some new breakthroughs for the mission. Relation extraction has theoretical significance, and has wide application prospect

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Conclusion