NDLI: Research on CNN + LSTM user intention classification based on multi-granularity features of texts

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Research on CNN + LSTM user intention classification based on multi-granularity features of texts

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Research on CNN + LSTM user intention classification based on multi-granularity features of texts

Content Provider	IET Digital Library
Author	Li, Chunhu Zhang, Chunying Fu, Qifeng
Abstract	Mobile communication companies have a large number of online customer interaction records. However, it is difficult for those companies to identify customer appeals and call preferences accurately, which makes it hard to apply massive amounts of data to product services. Firstly, all the texts in the original text library are processed at character-level and word-level granularities, then the character sequence library and the word sequence library are constructed, respectively. The text vector training tool is used to train the sequence library to obtain the text vector model. Secondly, a multi-granularity user intention classification model is constructed, which combines convolutional neural network (CNN) and long short-term memory and includes the text feature of character-level and word-level granularity. The results of single model classification at different granularities are fused according to the corresponding weights to achieve efficient user intention classification. Finally, the model is cross-tested using the k-fold method on the data set provided by China Mobile Communications Corporation and reached 87.6% accuracy in the classification task covering 66 categories of user intentions. Experiments show that the multi-granularity feature fusion model has higher accuracy than single-granularity feature models.
Starting Page	486
Ending Page	490
Page Count	5
Volume Number	2020
e-ISSN	20513305
Issue Number	Issue 13, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2020/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1175
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-01-13
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Call Preferences Character Sequence Library Character-level Granularity China Mobile Communication Corporation CNN Convolutional Neural Nets Customer Services Document Processing Technique Feature Extraction Long Short-term Memory LSTM Marketing Computing Mobile Communication Mobile Communication Companies Multigranularity Text Feature Multigranularity User Intention Classification Model Natural Language Processing Neural Computing Technique Online Customer Interaction Records Pattern Classification Product Services Recurrent Neural Nets Telecommunication Industry Text Analysis Text Vector Training Tool Word Sequence Library Word-level Granularity
Content Type	Text
Resource Type	Article

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