Entity Factor: A Balanced Method for Table Filling in Joint Entity and Relation Extraction

Author:

Liu ZhifengORCID,Tao Mingcheng,Zhou Conghua

Abstract

The knowledge graph is an effective tool for improving natural language processing, but manually annotating enormous amounts of knowledge is expensive. Academics have conducted research on entity and relation extraction techniques, among which, the end-to-end table-filling approach is a popular direction for achieving joint entity and relation extraction. However, once the table has been populated in a uniform label space, a large number of null labels are generated within the array, causing label-imbalance problems, which could result in a tendency of the model’s encoder to predict null labels; that is, model generalization performance decreases. In this paper, we propose a method to mitigate non-essential null labels in matrices. This method utilizes a score matrix to calculate the count of non-entities and the percentage of non-essential null labels in the matrix, which is then projected by the power of natural constant to generate an entity-factor matrix. This is then incorporated into the scoring matrix. In the back-propagation process, the gradient of non-essential null-labeled cells in the entity factor layer is affected and shrinks, the amplitude of which is related to the size of the entity factor, thereby reducing the feature learning of the model for a large number of non-essential null labels. Experiments with two publicly available benchmark datasets show that the incorporation of entity factors significantly improved model performance, especially in the relation extraction task, by 1.5% in both cases.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Reference29 articles.

1. Sang, E.F.T.K. (September, January 24). Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition. Proceedings of the International Conference on Computational Linguistics Association for Computational Linguistics, Taipei, Taiwan.

2. Bunescu, R., and Mooney, R. (2005, January 6–8). A shortest path dependency kernel for relation extraction. Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (EMNLP), Vancouver, BC, Canada.

3. Florian, R., Jing, H., Kambhatla, N., and Zitouni, I. (2006, January 17–18). Factorizing complex models: A case study in mention detection. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia.

4. Chan, Y.S., and Roth, D. (2011, January 19–24). Exploiting syntactico-semantic structures for relation extraction. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA.

5. Li, Q., and Ji, H. (2014, January 22–27). Incremental joint extraction of entity mentions and relations. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, MD, USA.

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