Abstract
Question Answering (QA) is undoubtedly a growing field of current research in Artificial Intelligence. Question classification, a QA subtask, aims to associate a category to each question, typically representing the semantic class of its answer. This step is of major importance in the QA process, since it is the basis of several key decisions. For instance, classification helps reducing the number of possible answer candidates, as only answers matching the question category should be taken into account. This paper presents and evaluates a rule-based question classifier that partially founds its performance in the detection of the question headword and in its mapping into the target category through the use of WordNet. Moreover, we use the rule-based classifier as a features’ provider of a machine learning-based question classifier. A detailed analysis of the rule-base contribution is presented. Despite using a very compact feature space, state of the art results are obtained.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Amaral C, Cassan A, Figueira H, Martins A, Mendes A, Mendes P, Pinto C, Vidal D (2008) Priberam’s question answering system in QA@CLEF 2007. In: Advances in multilingual and multimodal information retrieval: 8th workshop of the cross-language evaluation forum, CLEF 2007, Budapest, Hungary, September 19–21, 2007. Revised Selected Papers, pp 364–371, Berlin, Heidelberg, 2008. Springer. ISBN 978-3-540-85759-4 http://dx.doi.org/10.1007/978-3-540-85760-0_46
Bhagat R, Leuski A, Hovy E (2005) Shallow semantic parsing despite little training data. In: Proceedings of the ACL/SIGPARSE 9th international workshop on parsing technologies. Vancouver, Canada
Blunsom P, Kocik K, Curran JR (2006) Question classification with log-linear models. In: SIGIR ’06: proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, USA, pp 615–616. ISBN 1-59593-369-7. http://doi.acm.org/10.1145/1148170.1148282
Carlson AJ, Cumby CM, Rosen JL, Roth D (1999) Snow user guide. Technical report UIUC-DCS-R-99-210, Champaign, IL
Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines, Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Collins MJ (1999) Head-driven statistical models for natural language parsing. PhD thesis, Philadelphia, PA, USA
Fellbaum C (ed) (1998) WordNet: an electronic lexical database. MIT, Cambridge. URL http://books.google.es/books?hl=es&lr=&id=Rehu8OOzMIMC&oi=fnd&pg=PR11
Hermjakob U, Hovy E, Lin C-Y (2002) Automated question answering in Webclopedia: a demonstration. In: Proceedings of the second international conference on human language technology research, Morgan Kaufmann Publishers Inc, San Francisco, pp 370–371
Huang Z, Thint M, Qin Z (2008) Question classification using head words and their hypernyms. In: EMNLP, pp 927–936
Judge J, Cahill A, van Genabith J (2006) Questionbank: creating a corpus of parse-annotated questions. In: ACL-44: proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Morristown, NJ, USA, pp 497–504. http://dx.doi.org/10.3115/1220175.1220238
Krishnan V, Das S, Chakrabarti S (2005) Enhanced answer type inference from questions using sequential models. In: HLT ’05: proceedings of the conference on human language technology and empirical methods in natural language processing, Association for Computational Linguistics, Morristown, NJ, USA, pp 315–322. http://dx.doi.org/10.3115/1220575.1220615
Kwok CCT, Etzioni O, Weld DS (2001) Scaling question answering to the web. In: WWW ’01: proceedings of the 10th international conference on World Wide Web, ACMNew York, NY, USA, pp 150–161. ISBN 1-58113-348-0. http://doi.acm.org/10.1145/371920.371973
Li Fangtao, Zhang Xian, Yuan Jinhui, Zhu Xiaoyan (August 2008) Classifying what-type questions by head noun tagging. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 481–488, Manchester, UK, Coling 2008 Organizing Committee. URL http://www.aclweb.org/anthology/C08-1061.
Li X, Roth D (2002) Learning question classifiers. In: Proceedings of the 19th international conference on computational linguistics, Association for Computational Linguistics, Morristown, NJ, USA, pp 1–7. http://dx.doi.org/10.3115/1072228.1072378
Mendes A, Coheur L, Mamede NJ, Ribeiro RD, de Matos DM, Batista F (2008) QA@L2F, first steps at QA@CLEF. In: Advances in multilingual and multimodal information retrieval, volume 5152 of lecture notes in computer science. Springer, Berlin
Metzler D, Croft WB (2005) Analysis of statistical question classification for fact-based questions. Inf Retr 8(3): 481–504
Moldovan D, Paşca M, Harabagiu S, Surdeanu Mihai (2003) Performance issues and error analysis in an open-domain question answering system. ACM Trans Inf Syst 21(2): 133–154
Moldovan DI, Harabagiu SM, Paşca M, Mihalcea R, Girju R, Goodrum R, Rus V (2000) The structure and performance of an open-domain question answering system. In: ACL
Newell A (1990) Unified theories of cognition. Harvard University Press, Harvard
Pan Y, Tang Y, Lin L, Luo Y (2008) Question classification with semantic tree kernel. In: SIGIR ’08: proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, ACM, New York, NY, USA, pp 837–838. ISBN:978-1-60558-164-4 http://doi.acm.org/10.1145/1390334.1390530
Petrov S, Klein D (2007) Improved inference for unlexicalized parsing. In: Human language technologies 2007: the conference of the North American chapter of the Association for Computational Linguistics; proceedings of the main conference, Association for Computational Linguistics, Rochester, New York, pp 404–411. URL http://www.aclweb.org/anthology/N/N07/N07-1051
Saquete E, Vicedo JL, Martínez-Barco P, Muñoz R, Llorens H (2009) Enhancing QA systems with complex temporal question processing capabilities. J Artif Intell Res 35: 299–330
Sharada BA, Girish PM (2004) Wordnet has no ‘recycle bin’
Simon Herbert A (1969) The sciences of the artificial, 1st edn. MIT, Cambridge
Sun R (1995) A two-level hybrid architecture for structuring knowledge for commonsense reasoning. In: Sun R, Bookman LA (eds) Computational architectures integrating neural and symbolic processing, chap 8. Kluwer, Dordrecht, pp 247–282
Tarski A. (1956) Logic, semantics, metamathematics. Oxford University Press, London
Zhang D, Lee WS (2003) Question classification using support vector machines. In: SIGIR ’03: proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, ACM, New York, NY, USA, pp 26–32. ISBN:1-58113-646-3. http://doi.acm.org/10.1145/860435.860443
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Silva, J., Coheur, L., Mendes, A.C. et al. From symbolic to sub-symbolic information in question classification. Artif Intell Rev 35, 137–154 (2011). https://doi.org/10.1007/s10462-010-9188-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-010-9188-4