Abstract
Semantic role labeling (SRL) is a natural language processing (NLP) task that finds shallow semantic representations from sentences. In this paper, we construct a biomedical proposition bank and train a biomedical semantic role labeling system that can be used to facilitate relation extraction and information retrieval in biomedical domain. Firstly, we construct a proposition bank on the basis of the GENIA TreeBank following the Penn PropBank annotation. Secondly, we use GenPropBank to train a biomedical SRL system, which uses maximum entropy as a classifier. Our experimental results show that a newswire SRL system that achieves an F1 of 85.56 % in the newswire domain can only maintain an F1 of 65.43 % when ported to the biomedical domain. By using our annotated biomedical corpus, we can increase that F1 by 19.2 %.
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Acknowledgement
This work is supported by the Natural Science Foundation of China under Grant Nos. 61173095, 61202304.
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Han, L., Ji, Dh., Ren, H. (2015). Semantic Role Labeling for Biomedical Corpus Using Maximum Entropy Classifier. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_68
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DOI: https://doi.org/10.1007/978-3-319-22053-6_68
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