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Using Distance Graphs to Find Meaningful Levels of a Hierarchical Sequence Prior to Performing a Cluster Analysis Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Collectives of Term Weighting Methods for Natural Language Call Routing

Authors : Roman Sergienko, Tatiana Gasanova, Eugene Semenkin, Wolfgang Minker

Published in: Informatics in Control, Automation and Robotics

Publisher: Springer International Publishing

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Abstract

The paper presents the investigation of collectives of term weighting methods for natural language call routing. The database consists of user utterances recorded in English language from caller interactions with commercial automated agents. Utterances from this database are labelled by experts and divided into 20 classes. Seven different unsupervised and supervised term weighting methods were tested and compared with each other for classification with k-NN. Also a novel feature extraction method based on terms belonging to classes was applied. After that different combinations of term weighting methods were formed as collectives and used for meta-classification with rule induction. The numerical experiments have shown that the combination of two best term weighting methods (Term Relevance Ratio and Confident Weights) increases classification effectiveness in comparison with the best individual term weighting method significantly.

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Metadata
Title
Collectives of Term Weighting Methods for Natural Language Call Routing
Authors
Roman Sergienko
Tatiana Gasanova
Eugene Semenkin
Wolfgang Minker
Copyright Year
2016
Book
Informatics in Control, Automation and Robotics

Print ISBN: 978-3-319-26451-6

Electronic ISBN: 978-3-319-26453-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-26453-0_6