Abstract
Discourse structure recognition is an important aspect of the discourse structure rationality research. However, the assessment of discourse structure recognition usually suffer from the problems such as poor theory and lack of professional Chinese discourse structure corpus. We propose a discourse structure recognition model that consisted by the feature extraction method of “part-of-speech ratio & variance & Doc2vec” and three text classification methods that are Conditional Random Field (CRF), Support Vector Machine (SVM) and Naive Bayes for identifying discourse structure composition. We did two experiments to test the precision of the model and the highest precision reached 70.01%. What’s more, we did an experiment to study the effects of five types of paragraph labels on the model and the highest precision reached 82.13%. All experiments uses our own composition corpus of primary and secondary school students. Experimental results on datasets demonstrate that our model can be feasible and effective on research on discourse structure composition.
Supported by Key Special Projects of National Key R&D Program of China (2018YFC0830100), National Natural Science Foundation of China (61672361), and Beijing Municipal Education Commission-Beijing Natural Fund Joint Funding Project (KZ201910028039).
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Li, L., Liu, J., Han, X., Tan, X. (2019). The Discourse Structure Recognition Model Based on Text Classification. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11656. Springer, Cham. https://doi.org/10.1007/978-3-030-26354-6_25
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DOI: https://doi.org/10.1007/978-3-030-26354-6_25
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