An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Xiaoyu Han,Wenkai Zhang,Yue Zhang,Tinglei Huang

doi:10.3390/info11020079

Abstract

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

Highlights

Relation extraction aims to identify the relationship between two specified entities in a sentence.For example, from the sentence “LeBron James was born in Akron, Ohio.”, we can get triple informaiton (LeBron James, Birthplace, Akron)
We propose an attention-based model to verify the effectiveness of the character information provided by entity compositions in the Chinese relation extraction task
Since the attention-based models improve the performance of many NLP tasks, attention is used in relation classification

Summary

Introduction

From the sentence “LeBron James was born in Akron, Ohio.”, we can get triple informaiton (LeBron James, Birthplace, Akron). Since it was put forward, relation extraction has been one of the most critical tasks in NLP (Nature Language Processing) and played a crucial role in QA (Question-Answer), Knowledge Graph construction, and many other applications. There have been many studies in relation extraction, both in English and other languages. These methods show a trend from initial rule-based methods, traditional feature-based models, such as SVM (Support Vector Machine) [1] and probabilistic graphical models [2], to neural network-based. The research focus changes from supervised learning to distant supervised learning [5]

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