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Passage Re-ranking Method Based on Sentence Similarity Through Multitask Learning
Youngjin Jang, Hyeon-gu Lee, Jihyun Wang, Chunghee Lee, Harksoo Kim
http://doi.org/10.5626/JOK.2020.47.4.416
The machine reading comprehension(MRC) system is a question answering system in which a computer understands a given passage and respond questions. Recently, with the development of the deep neural network, research on the machine reading system has been actively conducted, and the open domain machine reading system that identifies the correct answer from the results of the information retrieval(IR) model rather than the given passage is in progress. However, if the IR model fails to identify a passage comprising the correct answer, the MRC system cannot respond to the question. That is, the performance of the open domain MRC system depends on the performance of the IR model. Thus, for an open domain MRC system to record high performance, a high performance IR model must be preceded. The previous IR model has been studied through query expansion and reranking. In this paper, we propose a re-ranking method using deep neural networks. The proposed model re-ranks the retrieval results (passages) through multi-task learning-based sentence similarity, and improves the performance by approximately 8% compared to the performance of the existing IR model with experimental results of 58,980 pairs of MRC data.
Competition Relation Extraction based on Combining Machine Learning and Filtering
ChungHee Lee, YoungHoon Seo, HyunKi Kim
This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new algorithm integrating machine learning with various filtering methods. Some simple but useful features for competition relation extraction are also introduced, and an optimum feature set is proposed. The goal of this paper was to increase the precision of competition relation extraction by combining supervised learning with various filtering methods. Filtering methods were employed for classifying compete relation occurrence, using distance restriction for the filtering of feature pairs, and classifying whether or not the candidate entity pair is spam. For evaluation, a test set consisting of 2,565 sentences was examined. The proposed method was compared with the rule-based method and general relation extraction method. As a result, the rule-based method achieved positive precision of 0.812 and accuracy of 0.568, while the general relation extraction method achieved 0.612 and 0.563, respectively. The proposed system obtained positive precision of 0.922 and accuracy of 0.713. These results demonstrate that the developed method is effective for competition relation extraction.
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