Skip to main content

Detecting Non-covered Questions in Frequently Asked Questions Collections

  • Conference paper
  • First Online:
  • 1820 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10260))

Abstract

Frequently asked questions (FAQ) collections are a popular and effective way of representing information, and FAQ retrieval systems provide a natural-language interface to such collections. An important aspect of efficient and trustworthy FAQ retrieval is to maintain a low fall-out rate by detecting non-covered questions. In this paper we address the task of detecting non-covered questions. We experiment with threshold-based methods as well as unsupervised one-class and supervised binary classifiers, considering tf-idf and word embeddings text representations. Experiments, carried out on a domain-specific FAQ collection, indicate that a cluster-based model with query paraphrases outperforms threshold-based, one-class, and binary classifiers.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Burke, R.D., Hammond, K.J., Kulyukin, V., Lytinen, S.L., Tomuro, N., Schoenberg, S.: Question answering from frequently asked question files: experiences with the FAQ finder system. AI Mag. 18(2), 57 (1997)

    Google Scholar 

  2. Sneiders, E.: Automated FAQ answering with question-specific knowledge representation for web self-service. In: 2nd Conference on Human System Interactions 2009, HSI 2009, pp. 298–305. IEEE (2009)

    Google Scholar 

  3. Surdeanu, M., Ciaramita, M., Zaragoza, H.: Learning to rank answers to non-factoid questions from web collections. Comput. Linguist. 37(2), 351–383 (2011)

    Article  Google Scholar 

  4. Feng, M., Xiang, B., Glass, M.R., Wang, L., Zhou, B.: Applying deep learning to answer selection: a study and an open task. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 813–820. IEEE (2015)

    Google Scholar 

  5. Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)

    Article  MATH  Google Scholar 

  6. Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Signal Process. 99, 215–249 (2014)

    Article  Google Scholar 

  7. Wieting, J., Bansal, M., Gimpel, K., Livescu, K., Roth, D.: From paraphrase database to compositional paraphrase model and back. Trans. Assoc. Comput. Linguist. 3, 345–358 (2015)

    Google Scholar 

  8. Karan, M., Šnajder, J.: FAQIR – a frequently asked questions retrieval test collection. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2016. LNCS, vol. 9924, pp. 74–81. Springer, Cham (2016). doi:10.1007/978-3-319-45510-5_9

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mladen Karan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Karan, M., Šnajder, J. (2017). Detecting Non-covered Questions in Frequently Asked Questions Collections. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59569-6_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59568-9

  • Online ISBN: 978-3-319-59569-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics