dc.contributor.author | Rustamov, Samir | |
dc.contributor.author | Mustafayev, Elshan | |
dc.contributor.author | Clements, Mark A. | |
dc.date.accessioned | 2022-05-12T06:51:00Z | |
dc.date.available | 2022-05-12T06:51:00Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12181/371 | |
dc.description.abstract | The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM) can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to non-usage of lexical or syntactic analysis in classification process. | en_US |
dc.language.iso | en | en_US |
dc.publisher | De Gruyter | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject.lcsh | Natural Language Call Routing. | en |
dc.subject.lcsh | Text Mining. | en |
dc.subject.lcsh | ANFIS. | en |
dc.subject.lcsh | HMM. | en |
dc.title | Context Analysis of Customer Requests using a Hybrid Adaptive Neuro Fuzzy Inference System and Hidden Markov Models in the Natural Language Call Routing Problem | en_US |
dc.type | Article | en_US |
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