A Memetic-Based Approach for Web-Based Question Answering

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Author(s)

Iman Khodadi 1,* Mohammad Saniee Abadeh 1

1. Faculty of Electrical and Computer Engineering of Tarbiat Modares University, Tehran, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2014.09.05

Received: 1 Dec. 2013 / Revised: 11 Mar. 2014 / Accepted: 10 May 2014 / Published: 8 Aug. 2014

Index Terms

Question Answering, Memetic Algorithm, Information Extraction (IE), Natural Language Processing (NLP), Local Search, Evolutionary Computing, Dynamic Mutation Ratio

Abstract

In this paper we proposed an evolutionary approach for answering open-domain factoid questions, which include searching among sentences that are candidate for the final answer with Memetic Algorithm (MA), and using lexical and syntactic features for calculating fitness of the sentences. Our main purpose is making a search engine with accurate answering ability, or a web-based Question Answering (QA) system. The Text Retrieval Conference (TREC) QA Tracks data are used to develop and evaluate the approach. The answering process begins with retrieving related documents from a search engine. Then, MA searches among all the sentences of these documents and finds the best one. Finally, one or more words will be extracted based on our hand-made patterns. The results of different approaches for local search, mutation, and crossover, and also different values for number of reproduction and retrieved documents are investigated in the empirical study section. The results are promising with sufficient retrieved documents, and we have obtained a threshold value for this variable. Using MA instead of examining all the sentences is a trade-off between lowering the process time and sacrificing the accuracy, but the results show that the Mametic-based approach is more efficient.

Cite This Paper

Iman Khodadi, Mohammad Saniee Abadeh, "A Memetic-Based Approach for Web-Based Question Answering", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.9, pp.39-45, 2014. DOI:10.5815/ijitcs.2014.09.05

Reference

[1]Nitin Indurkhya, Fred J. Damerau, Handbook of Natural Language Processing, 2nd ed., Chapman & Hall/CRC, 2010.

[2]Alexander Clark, Chris Fox, Shalom Lappin, The Handbook of Computational Linguistics and Natural Language Processing, Wiley-Blackwell, 2010.

[3]Kupiec, J., “MURAX: A Robust Linguistic Approach for Question-Answering Using an Online Encyclopedia”, In 16th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pittsburgh, PA, pp. 181–190, 1993.

[4]Boris Katz, Jimmy J. Lin, Sue Felshin, “The START Multimedia Information System: Current Technology and Future Directions”, in: Proceedings of the International Workshop on Multimedia Information Systems, pp. 117–123, 2002.

[5]Oleksandr Kolomiyets, Marie-Francine Moens, “A Survey on Question Answering Technology from an Information Retrieval Perspective”, Information Sciences, vol. 181, pp. 542–543, 2011.

[6]John Atkinson, Alejandro Figueroa, Christian Andrade, "Evolutionary Optimization for Ranking How-to Questions Based on User-generated Contents", Expert Systems with Applications, vol. 40, pp. 7060–7068, 2013.

[7]Enrique Alba, Gabriel Luque, Lourdes Araujo, “Natural Language Tagging with Genetic Algorithms”, Information Processing Letters, vol. 100, pp. 173–182, 2006.

[8]Tsai, C.-F., et al., “Evolutionary Instance Selection for Text Classification”, J. Syst. Software (2014), http://dx.doi.org/10.1016/j.jss.2013.12.034, in press.

[9]Mohsen Shakiba Fakhr, Mohammad Saniee Abadeh, "AISQA - An Artificial Immune Question Answering System", International Journal of Information Technology and Computer Science (IJMECS), vol. 4, No. 3, 2012.

[10]Matthias H. Heie, Edward W.D. Whittaker, Sadaoki Furui, “Question Answering Using Statistical Language Modeling”, Computer Speech and Language, vol. 26, pp. 193–209, 2012.

[11]Markov, A., “An Example of Statistical Investigation in the Text of Eugene Onegin Illustrating Coupling of Tests in Chains”. Proc Academy of Sciences of St. Petersburg, vol. 7, 1913.

[12]P. Moscato, “On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms”, Caltech concurrent computation program (report 826), 1989.

[13]Ferrante Neri, Carlos Cotta, “Memetic Algorithms and Memetic Computing Optimization: A Literature Review”, Swarm and Evolutionary Computation, vol. 2, pp. 1–14, 2012.

[14]T. C. Fogarty, “Varying the Probability of Mutation in the Genetic Algorithm”, In Proceedings of the third international conference on genetic algorithms, San Mateo, C. A., Morgan Kaufmann, pp. 104-109, 1989.

[15]Hoa Trang Dang, Diane Kelly, Jimmy Lin, “Overview of the TREC 2007 Question Answering Track”, In Proceedings of the Sixteenth Text Retrieval Conference, 2007.