Abstract
This paper investigates the potentialities of a lightweight approach to the Expected Answer Type (EAT) recognition task in a specific restricted-domain Question Answering scenario. In such scenario, the input is represented by automatically transcribed spoken requests, possibly affected by transcription errors. Our objective is to demonstrate that, when dealing with sub-optimal (i.e. noisy) inputs, good performance can be easily achieved with a Machine Learning approach based on simple features extracted from unprocessed questions. In contrast to traditional approaches dealing with questions pre-processed at different levels (including lemmatization, part of speech (POS) tagging, and multiword recognition), the advantage of our lightweight approach is that extra errors often derived from processing noisy data are avoided.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Li, X., Roth, D.: Learning question classifiers: the role of semantic information. Natural Language Engineering 12(3), 229–249 (2006)
Sekine, S.: Extended Named Entity Ontology with Attribute Information. In: Proceedings of the 6th edition of the Language Resources and Evaluation Conference (LREC 2008), Marrakech, Morocco (2008)
Gretter, R., Kouylekov, M., Negri, M.: Dealing with Spoken Requests in a Multimodal Question Answering System. In: Dochev, D., Pistore, M., Traverso, P. (eds.) AIMSA 2008. LNCS (LNAI), vol. 5253, pp. 93–102. Springer, Heidelberg (2008)
Pinchak, C., Lin, D.: A Probabilistic Answer Type Model. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006), Trento, Italy, April 3-7, pp. 393–400 (2006)
Echihabi, A., Oard, D.W., Marcu, D., Hermjakob, U.: Cross-language question answering at the USC Information Sciences Institute. In: Peters, C., Gonzalo, J., Braschler, M., Kluck, M. (eds.) CLEF 2003. LNCS, vol. 3237, pp. 514–522. Springer, Heidelberg (2004)
Mitkov, R. (ed.): The Oxford Handbook of Computational Linguistics. Oxford University Press, Oxford (2003)
Greenwood, M.A.: AnswerFinder: Question Answering from your Desktop. In: Proceedings of the 7th Annual Colloquium for the UK Special Interest Group for Computational Linguistics (CLUK 2004), January 6-7. University of Birmingham (2004)
Attardi, G., Cisternino, A., Formica, F., Simi, M., Tommasi, A.: PiQASso: Pisa Question Answering System. In: Proceedings of Text Retrieval Conference (Trec-10), November 13-16. NIST, Gaithersburg (2001)
Joachims, T.: Making large-Scale SVM Learning Practical. In: Schlkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1999)
Radev, D., Fan, W., Qi, H., Wu, H., Grewal, A.: Probablistic Question Answering on the Web. In: Proceedings of the Eleventh International World Wide Web Conference (2002)
Cabrio, E., Kouylekov, M., Magnini, B., Negri, M., Hasler, L., Orasan, C., Tomas, D., Vicedo, J.L., Neumann, G., Weber, C.: The QALL-ME Benchmark: a Multilingual Resource of Annotated Spoken Requests for Question Answering. In: Proceedings of the Language Resources and Evaluation Conference (LREC 2008), 6th edn., Marrakech, Morocco (2008)
Chen, J., Diekema, A.R., Taffet, M.D., McCracken, N., Ozgencil, N.E., Yilmazel, O., Liddy, E.D.: Question answering: CNLP at the TREC-10 question answering track. In: Proceedings of 10th Text REtrieval Conference (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chowdhury, M.F.M., Negri, M. (2009). Expected Answer Type Identification from Unprocessed Noisy Questions. In: Andreasen, T., Yager, R.R., Bulskov, H., Christiansen, H., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 2009. Lecture Notes in Computer Science(), vol 5822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04957-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-04957-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04956-9
Online ISBN: 978-3-642-04957-6
eBook Packages: Computer ScienceComputer Science (R0)