Learning User Intentions in Natural Language Call Routing Systems

2016 ◽  
pp. 37-46 ◽  
Author(s):  
Kamil Aida-zade ◽  
Samir Rustamov
Keyword(s):  
Natural Language ◽  
Call Routing ◽  
User Intentions

Boosting and combination of classifiers for natural language call routing systems

2003 ◽  
Vol 41 (4) ◽  
pp. 647-661 ◽  
Author(s):  
Imed Zitouni ◽  
Hong-Kwang Jeff Kuo ◽  
Chin-Hui Lee
Keyword(s):  
Natural Language ◽  
Call Routing ◽  
Combination Of Classifiers

Using a Dialogue Manager to Improve Semantic Web Search

2016 ◽  
Vol 12 (1) ◽  
pp. 62-78 ◽  
Author(s):  
Dora Melo ◽  
Irene Pimenta Rodrigues ◽  
Vitor Beires Nogueira
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
System Performance ◽  
Web Search ◽  
Question Answering ◽  
Similar Question ◽  
User Intentions ◽  
Question Answering Systems ◽  
Dialogue Manager

Question Answering systems that resort to the Semantic Web as a knowledge base can go well beyond the usual matching words in documents and, preferably, find a precise answer, without requiring user help to interpret the documents returned. In this paper, the authors introduce a Dialogue Manager that, through the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions, but also represents the structure of the discourse, including the user intentions and the questions context, adding the ability to deal with multiple answers and providing justified answers. The authors' system performance is evaluated by comparing with similar question answering systems. Although the test suite is slight dimension, the results obtained are very promising.

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