Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention

• URL: http://arxiv.org/abs/2010.13583v2
• Date: Thu, 28 Jan 2021 02:33:37 GMT
• Title: Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention
• Authors: Linlin Hou, Ji Zhang, Ou Wu, Ting Yu, Zhen Wang, Zhao Li, Jianliang Gao, Yingchun Ye, Rujing Yao
• Abstract summary: We propose a novel entity recognition model, called MDER, which is able to effectively extract the method and dataset entities from scientific papers. We evaluate the proposed model on datasets constructed from the published papers of four research areas in computer science, i.e., NLP, CV, Data Mining and AI.
• Score: 21.93889297841459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Literature analysis facilitates researchers to acquire a good understanding of the development of science and technology. The traditional literature analysis focuses largely on the literature metadata such as topics, authors, abstracts, keywords, references, etc., and little attention was paid to the main content of papers. In many scientific domains such as science, computing, engineering, etc., the methods and datasets involved in the scientific papers published in those domains carry important information and are quite useful for domain analysis as well as algorithm and dataset recommendation. In this paper, we propose a novel entity recognition model, called MDER, which is able to effectively extract the method and dataset entities from the main textual content of scientific papers. The model utilizes rule embedding and adopts a parallel structure of CNN and Bi-LSTM with the self-attention mechanism. We evaluate the proposed model on datasets which are constructed from the published papers of four research areas in computer science, i.e., NLP, CV, Data Mining and AI. The experimental results demonstrate that our model performs well in all the four areas and it features a good learning capacity for cross-area learning and recognition. We also conduct experiments to evaluate the effectiveness of different building modules within our model which indicate that the importance of different building modules in collectively contributing to the good entity recognition performance as a whole. The data augmentation experiments on our model demonstrated that data augmentation positively contributes to model training, making our model much more robust in dealing with the scenarios where only small number of training samples are available. We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.

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