Abstract
Sentiment classification on clinical narratives has been a groundwork to analyze patient’s health status, medical condition and treatment. The work posed challenges due to the shortness, and implicit sentiment of the clinical text. The paper shows that a sentiment score of a sentence simultaneously depends on scores of its terms including words, phrases, sequences of non-adjacent words, thus we propose to use a linear combination which can incorporate the scores of the terms extracted by various language models with the corresponding coefficients for estimating the sentence’s score. Through utilizing the linear combination, we derive a novel vector representation of a sentence called language-model-based representation that is based on average scores of kinds of term in the sentence to help supervised classifiers work more effectively on the clinical narratives.
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This work is partially funded by Vietnam National University at Ho Chi Minh City under the grant number B2015-42-02, and Japan Advanced Institute of Science and Technology under the Data Science Project.
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Dang, TT., Ho, TB. (2016). Mixture of Language Models Utilization in Score-Based Sentiment Classification on Clinical Narratives. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_22
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DOI: https://doi.org/10.1007/978-3-319-42007-3_22
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