Abstract
The analysis and understanding of spoken texts is an important task in artificial intelligence and natural language processing. However, there are many verbose expressions (such as mantras, nonsense, modal particle, etc.) in spoken texts, which brings great challenges to subsequent tasks. This paper devote to detect verbose expressions in spoken texts. Considering the correlation of verbose words/characters in spoken texts, we adapt sequence models to detect them with an end-to-end manner. Moreover, we propose a model with the long-short term memory (LSTM) and modified restrict attention (MRA) mechanism which are able to utilize the mutual influence between long-distance and local words in sentences. In addition, we propose a compare mechanism to model the repetitive verbose expressions. The experimental result shows that compared with the rule-based and direct classification methods, our proposed model increases F1 measure by 54.08% and 18.91%.
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Acknowledgments
The research work is supported by the National Key Research and Development Program of China under Grant No.2017YFB1002101, the Natural Science Foundation of China (No.61533018 and No.61702512), and the independent research project of National Laboratory of Pattern Recognition.
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Liu, Q., He, S., Liu, K., Liu, S., Zhao, J. (2018). Learning to Detect Verbose Expressions in Spoken Texts. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_30
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DOI: https://doi.org/10.1007/978-3-030-01716-3_30
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