Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Using Distance Graphs to Find Meaningful Levels of a Hierarchical Sequence Prior to Performing a Cluster Analysis Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Collectives of Term Weighting Methods for Natural Language Call Routing

verfasst von : Roman Sergienko, Tatiana Gasanova, Eugene Semenkin, Wolfgang Minker

Erschienen in: Informatics in Control, Automation and Robotics

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Adaptive Tracking Approach of Flexible Cable Conduit-Actuated NOTES Systems for Early Gastric Cancer Treatments
Nächstes Kapitel Evaluating Template Uniqueness in ECG Biometrics
Literatur
1.
Bengio, Y., Schwenk, H., Senecal, J.-S., Morin, F., and Gauvain, J.-L.: Neural probabilistic language models. In: Innovations in Machine Learning, 137–186 (2006)
2.
Cohen, W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, California (1995)
3.
Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, 160–167 (2008)
4.
Debole, F., Sebastiani, F.: Supervised term weighting for automated text categorization. Text mining and its applications, Springer, Berlin Heidelberg, 81–97 (2004)
5.
Gasanova, T., Sergienko, R., Minker, W., Semenkin, E., Zhukov, E.: A semi-supervised approach for natural language call routing. In: Proceedings of the SIGDIAL 2013 Conference, 344–348 (2013)
6.
Gasanova, T., Sergienko, R., Akhmedova, S., Semenkin, E., Minker, W.: Opinion mining and topic categorization with novel term weighting. In: Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistics, Baltimore, Maryland, USA, 84–89 (2014)
7.
Gasanova, T., Sergienko, R., Semenkin, E., Minker, W.: Dimension Reduction with Coevolutionary Genetic Algorithm for Text Classification. In: Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vienna University of Technology, Austria, Vol. 1, 215–222 (2014)
8.
Huang, F., Yates, A.: Distributional representations for handling sparsity in supervised sequencelabeling. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: ACL, Vol. 1, 495–503 (2009)
9.
Ishibuchi, H., Nakashima, T., Murata, T.: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 29(5), 601–618 (1999)CrossRef
10.
Joachims, T.: Learning to classify text using support vector machines: methods, theory and algorithms. Kluwer Academic Publishers, Berlin (2002)
11.
Ko, Y.: A study of term weighting schemes using class information for text classification. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1029–1030 (2012)
12.
Koo, T., Carreras, X., Collins, M.: Simple semisupervised dependency parsing. ACL, 595–603 (2008)
13.
Lan, M., Tan, C.L., Su, J., Lu, Y.: Supervised and traditional term weighting methods for automatic text categorization. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 721–735 (2009)CrossRef
14.
Miller, S., Guinness, J., Zamanian, A.: Name tagging with word clusters and discriminative training. HLT-NAACL 4, 337–342 (2004)
15.
Mnih, A. Hinton, G.: Three new graphical models for statistical language modelling. In: Proceedings of the 24th International Conference on Machine Learning, 641–648 (2007)
16.
Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning, ACL, 147–155 (2009)
17.
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inform. Process. manage. 24(5), 513–523 (1988)CrossRef
18.
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRef
19.
Shafait, F., Reif, M., Kofler, C., Breuel, T.M.: Pattern recognition engineering. RapidMiner Community Meeting and Conference, 9 (2010)
20.
Schwenk, H. Gauvain, J.-L.: Connectionist language modeling for large vocabulary continuous speech recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1 (2002)
21.
Soucy, P., Mineau, G.W.: Beyond TFIDF weighting for text categorization in the Vector space model. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), 1130–1135 (2005)
22.
Wilson, E.B.: Probable inference, the law of succession, and statistical inference. J. Am. Stat. Assoc. 22(158), 209–212 (1927)CrossRef
23.
Xu, H., Li, C.: A Novel term weighting scheme for automated text Categorization. Intelligent Systems Design and Applications (2007)
24.
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. ICML 9, 412–420 (1997)
Metadaten
Titel
Collectives of Term Weighting Methods for Natural Language Call Routing
verfasst von
Roman Sergienko
Tatiana Gasanova
Eugene Semenkin
Wolfgang Minker
Copyright-Jahr
2016
Buch
Informatics in Control, Automation and Robotics

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

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

Copyright-Jahr: 2016

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

Neuer Inhalt

    Bildnachweise