A Semieager Classifier for Open Relation Extraction

A variety of open relation extraction systems have been developed in the last decade. And deep learning, especially with attention model, has gained much success in the task of relation classification. Nevertheless, there is, yet, no research reported on classifying open relation tuples to our knowl...

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Bibliographic Details
Published in:Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-9
Main Authors: Liu, Peiqian, Wang, Xiaojie
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Classification
Information processing
International conferences
Language
Linguistics
Machine learning
Mathematical problems
Natural language
Neural networks
Researchers
Semantic relations
Semantic web
Semantics
State of the art
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Summary:A variety of open relation extraction systems have been developed in the last decade. And deep learning, especially with attention model, has gained much success in the task of relation classification. Nevertheless, there is, yet, no research reported on classifying open relation tuples to our knowledge. In this paper, we propose a novel semieager learning algorithm (SemiE) to tackle the problem of open relation classification. Different from the eager learning approaches (e.g., ANNs) and the lazy learning approaches (e.g., kNN), the SemiE offers the benefits of both categories of learning scheme, with its significantly lower computational cost (O(n)). This algorithm can also be employed in other classification tasks. Additionally, this paper presents an adapted attention model to transform relation phrases into vectors by using word embedding. The experimental results on two benchmark datasets show that our method outperforms the state-of-the-art methods in the task of open relation classification.
ISSN:1024-123X
1563-5147
DOI:10.1155/2018/4929674