Abstract
Sci-tech compound phrase entity (e.g., the names of projects, books and patents) recognition is a fundamental task of science and technology data processing and discovery. However, much less work have been done on the problem. In this paper, we first give the characteristics of sci-tech entities that are different from personal name and other traditional entities. Then we introduce a self-learning rule-based approach to address the problem. The approach consists of three stages, namely rule-based text truncation, BlackPOS-based text split and WhiteKey-based confirmation. Constructing the best WhiteKey list is a NP-hard problem. We further design dynamic programming and greedy algorithms to address the problem. Experimental results show that our approach achieves 94.78% precision rate, 89.19% recall rate and 91.9% F 1 measure in average. Moreover, our approach is universal and orthogonal to prior named entity recognition work.
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Liu, T., Zhang, Y., Yan, Y., Shi, J., Guo, L. (2015). A Self-learning Rule-Based Approach for Sci-tech Compound Phrase Entity Recognition. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_60
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DOI: https://doi.org/10.1007/978-3-319-25255-1_60
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