International Journal of Speech Technology

, Volume 15, Issue 2, pp 151–164 | Cite as

The Construction-Integration framework: a means to diminish bias in LSA-based call routing

  • Guillermo Jorge-BotanaEmail author
  • Ricardo Olmos
  • Alejandro Barroso


Semantic technology is commonly used for two purposes in the field of IVR (Interactive Voice Response). The first is to correct the output of voice recognition devices based on coherence with a context. The second is to perform what is referred to as “call routing”, requiring technology that categorizes utterances and returns a list of the most credible routes. Our paper focuses on the latter, aiming to use the Latent Semantic Analysis (LSA henceforth) computational model (Deerwester et al. in J. Am. Soc. Inf. Sci. 41:391–407, 1990) together with the Construction-Integration model (C-I henceforth), a psycholinguistically motivated algorithm (Kintsch in Int. J. Psychol. 33(6):411–420, 1998), to interpret, manage and successfully route user requests in an efficient and reliable manner. By efficient we mean that training is unnecessary when the destination model is altered, and exhaustive labeling of all utterances is not required, concentrating instead only on some sample destinations. By reliable we mean that the construction-integration algorithm attenuates the risks from intra-destination variability and word saliency. Technical and theoretical aspects are discussed. In addition, some destination assignment methods are tested and debated.


Call routing Call steering Natural language Latent semantic analysis Construction-Integration Psycholinguistically motivated algorithms 


  1. Bellegarda, J. R. (2000). Exploiting latent semantic information in statistical language modeling. Proceedings of the IEEE, 88(8), 1279–1296. CrossRefGoogle Scholar
  2. Chu-Carroll, J., & Carpenter, B. (1999). Vector-based natural language call routing. Computational Linguistics, 25(3), 361–388. Google Scholar
  3. Cox, S., & Shahshahani, B. (2001). A comparison of some different techniques for vector based call-routing. In Proceedings of 7th European conf. on speech communication and technology, Aalborg. Google Scholar
  4. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41, 391–407. CrossRefGoogle Scholar
  5. Foltz, P. W. (1996). Latent semantic analysis for text-based research. Behavior Research Methods, Instruments, & Computers, 28(2), 197–202. CrossRefGoogle Scholar
  6. Haley, D. T., Thomas, P., De Roeck, A., & Petre, M. (2005). A research taxonomy for latent semantic analysis based educational applications. Technical Report no. 2005/09, Open University. Google Scholar
  7. Haley, D. T., Thomas, P., Petre, P., & De Roeck, A. (2007). Seeing the whole picture: comparing computer assisted assessment systems using LSA-based systems as an example. Technical Report Number 2007/07, Open University. Google Scholar
  8. Jones, M. P., & Martin, J. H. (1997). Contextual spelling correction using latent semantic analysis. In Proceedings of the fifth conference on applied natural language processing (pp. 163–176). Google Scholar
  9. Jorge-Botana, G., Olmos, R., & León, J. A. (2009). Using LSA and the predication algorithm to improve extraction of meanings from a diagnostic corpus. Spanish Journal of Psychology, 12(2), 424–440. Google Scholar
  10. Jorge-Botana, G., León, J. A., Olmos, R., & Escudero, I. (2010a). Latent semantic analysis parameters for essay evaluation using small-scale corpora. Journal of Quantitative Linguistics, 17(1), 1–29. CrossRefGoogle Scholar
  11. Jorge-Botana, G., León, J. A., Olmos, R., & Hassan-Montero, Y. (2010b). Visualizing polysemy using LSA and the predication algorithm. Journal of the American Society for Information Science and Technology, 61(8), 1706–1724. Google Scholar
  12. Jorge-Botana, G., León, J. A., Olmos, R., & Escudero, I. (2011). The representation of polysemy through vectors: some building blocks for constructing models and applications with LSA. International Journal of Continuing Engineering Education and Long Learning, 21(4). Google Scholar
  13. Kintsch, W. (1998). The representation of knowledge in minds and machines. International Journal of Psychology, 33(6), 411–420. CrossRefGoogle Scholar
  14. Kintsch, W. (2000). Metaphor comprehension: a computational theory. Psychonomic Bulletin & Review, 7, 257–266. CrossRefGoogle Scholar
  15. Kintsch, W. (2001). Predication. Cognitive Science, 25, 173–202. CrossRefGoogle Scholar
  16. Kintsch, W. (2002). On the notions of theme and topic in psychological process models of text comprehension. In M. Louwerse & W. van Peer (Eds.), Thematics: interdisciplinary studies (pp. 157–170). Amsterdam: Benjamins. Google Scholar
  17. Kintsch, W. (2007). Meaning in context. In T. K. Landauer, D. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of latent semantic analysis (pp. 89–105). Mahwah: Erlbaum. Google Scholar
  18. Kintsch, W. (2008). Symbol systems and perceptual representations. In M. de Vega, A. M. Glenberg, & A. C. Graesser (Eds.), Symbols and embodiment: debates on meaning and cognition (pp. 145–164). Oxford: Oxford University Press. CrossRefGoogle Scholar
  19. Kintsch, W., & Bowles, A. (2002). Metaphor comprehension: what makes a metaphor difficult to understand? Metaphor and Symbol, 17, 249–262. CrossRefGoogle Scholar
  20. Kintsch, W., & Welsch, D. (1991). The construction-integration model: a framework for studying memory for text. In W. E. Hockley & S. Lewandowsky (Eds.), Relating theory and data: essays on human memory in honor of Bennet B. Murdock (pp. 367–385). Hillsdale: Erlbaum. Google Scholar
  21. Kintsch, W., Patel, V., & Ericsson, K. A. (1999). The role of long-term working memory in text comprehension. Psychologia, 42, 186–198. Google Scholar
  22. Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: the latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211–240. CrossRefGoogle Scholar
  23. Li, L., & Chou, W. (2002). Improving latent semantic indexing based classifier with information gain. In Proceedings of the 7th international conference on spoken language processing, ICSLP-2002, Denver, Colorado, USA, September 16–20, 2002 (pp. 1141–1144). Google Scholar
  24. Lim, B. P., Ma, B., & Li, H. (2005). Using semantic context to improve voice keyword mining. In Proceedings of the international conference on Chinese computing (ICCC 2005), Singapore, 21–23 March 2005. Google Scholar
  25. Louwerse, M. M. (2008). Embodied representations are encoded in language. Psychonomic Bulletin & Review, 15, 838–844. CrossRefGoogle Scholar
  26. Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge: MIT Press. zbMATHGoogle Scholar
  27. McCauley, L. (unpublished). Using latent semantic analysis to aid speech recognition and understanding.
  28. Nakov, P., Popova, A., & Mateev, P. (2001). Weight functions impact on LSA performance. In Proceedings of the recent advances in natural language processing conference—RANLP 2001, Tzigov Chark, Bulgaria. Google Scholar
  29. Olmos, R., León, J. A., Jorge-Botana, G., & Escudero, I. (2009). New algorithms assessing short summaries in expository texts using latent semantic analysis. Behavior Research Methods, 41(3), 944–950. CrossRefGoogle Scholar
  30. Quesada, J. (2008). Latent problem solving analysis (LPSA): a computational theory of representation in complex, dynamic problem solving tasks. PhD thesis, Psychology, University of Granada. Google Scholar
  31. Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New York: McGrawHill. zbMATHGoogle Scholar
  32. Serafin, R., & Di Eugenio, B. (2004). FLSA: extending latent semantic analysis with features for dialogue act classification. In Proceedings of ACL04, 42nd annual meeting of the association for computational linguistics Barcelona, Spain, July. Google Scholar
  33. Shi, Y. (2008). An investigation of linguistic information for speech recognition error detection. PhD University of Maryland, Baltimore County, Baltimore. Google Scholar
  34. Tyson, N., & Matula, V. C. (2004). Improved LSI-based natural language call routing using speech recognition confidence scores. In Proceedings of EMNLP. Google Scholar
  35. Wild, F., Haley, D., & Bülow, K. (2011). Using latent-semantic analysis and network analysis for monitoring conceptual development. Journal for Language Technology and Computational Linguistics, 26(1), 9–21. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Guillermo Jorge-Botana
    • 1
    Email author
  • Ricardo Olmos
    • 2
  • Alejandro Barroso
    • 3
  1. 1.Departamento de Psicología Evolutiva y de la Educación, Facultad de PsicologíaUniversidad Nacional de Educación a Distancia (UNED)MadridSpain
  2. 2.Departamento de Metodología de las Ciencias del Comportamiento, Facultad de PsicologíaUniversidad Autónoma de MadridMadridSpain
  3. 3.PlusNet SolutionsMadridSpain

Personalised recommendations