Abstract
The goal of relation extraction is to obtain relational facts from plain text, which can benefit a variety of natural language processing tasks. To address the challenge of automatically labeling large-scale training data, a distant supervision strategy is introduced to relation extraction by heuristically aligning entity pairs in plain text with the knowledge base. Unfortunately, the method is vulnerable to the noisy label problem due to the incompletion of the exploited knowledge base. Existing works focus on the specific algorithms, but few works summarize the commonalities between different methods and the influencing factors of these denoising mechanisms. In this paper, we propose three main factors that impact the label denoising of distantly supervised relation extraction, including labeling assumption, prior knowledge and confidence level. In order to analyze how these factors influence the denoising effectiveness, we build a unified neural framework with word, sentence and label denoising modules for relation extraction. Then we conduct experiments to evaluate and compare these factors according to ten neural schemes. In addition, we discuss the typical cases of these factors and find that influential word-level prior knowledge and partial confidence for distantly supervised labels can significantly affect the denoising performance. These implicational findings can provide researchers with more insight of distantly supervised relation extraction.
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This work is supported by National Natural Science Foundation of China, 61602048, 61520106007, BUPT-SICE Excellent Graduate Students Innovation Funds, 2016.
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Sun, T., Zhang, C., Ji, Y. (2018). Factors Impacting the Label Denoising of Neural Relation Extraction. In: Tang, S., Du, DZ., Woodruff, D., Butenko, S. (eds) Algorithmic Aspects in Information and Management. AAIM 2018. Lecture Notes in Computer Science(), vol 11343. Springer, Cham. https://doi.org/10.1007/978-3-030-04618-7_2
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