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A Review of the State of the Art in Hindi Question Answering Systems

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Intelligent Natural Language Processing: Trends and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 740))

Abstract

Question Answering Systems (QAS) are tools to retrieve precise answers for user questions from a large set of text documents. Researchers from information retrieval and natural language processing community have put tremendous efforts to improve the performance of QASs across several languages. However, Hindi, the fourth most spoken language has not seen a proportional development in the field of question answering to an extent that information seekers accept QASs as a good alternative of search engines. In this chapter, a pipelined architecture for the development of QASs has been explained in the context of English and Hindi languages. This chapter also reviews the developments taking place in Hindi QASs while explaining the challenges faced by researchers in developing Hindi QASs. To encourage and support the new researchers in conducting researches in Hindi QASs, a list of techniques, tools and linguistic resources required to implement the components of a QAS are described in this chapter in a simple and persuasive manner. Finally, the future directions for research in Hindi QASs have been proposed.

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Notes

  1. 1.

    Notice that one question can be paraphrased and asked using more than one pattern, getting more than one surface form that share the same meaning. For example the following questions should get the same answer: “During which month tourists visit Kashmir the most?”, “What month do tourists visit Kashmir the most?”, “Which month do tourists visit Kashmir the most?”, and “When do tourists visit Kashmir the most?”.

  2. 2.

    List of Hindi stopwords, http://members.unine.ch/jacques.savoy/clef/hindiST.txt.

  3. 3.

    A shallow parser, http://ltrc.iiit.ac.in/showfile.php?filename=downloads/shallow_parser.php.

  4. 4.

    A Hindi stemmer, e http://research.variancia.com/hindi_stemmer/.

  5. 5.

    Hindi POS Tagger, http://sivareddy.in/downloads#hindi_tools.

  6. 6.

    Apache OpenNLP, http://opennlp.apache.org/download.html.

  7. 7.

    Pre-trained models for OpenNLP, http://opennlp.sourceforge.net/models-1.5/.

  8. 8.

    WordNet, http://wordnet.princeton.edu/wordnet/download/.

  9. 9.

    Hindi WordNet, http://www.cfilt.iitb.ac.in/wordnet/webhwn/.

  10. 10.

    Python implementation of Hindi WordNet, http://sivareddy.in/downloads#python-hindi-wordnet.

  11. 11.

    IndoWordNet, http://www.cfilt.iitb.ac.in/indowordnet/index.jsp.

  12. 12.

    Hindi Wikipedia, https://hi.wikipedia.org/wiki/िवशेष:/Statistics, accessed on January, 25, 2017.

  13. 13.

    DBPedia, http://wiki.dbpedia.org/about, accessed on January, 25, 2017.

  14. 14.

    HindiWalC corpus, https://www.sketchengine.co.uk/hindiwac-corpus/.

  15. 15.

    Lucene, http://lucene.apache.org/core/.

  16. 16.

    Lucene classes for Hindi, https://lucene.apache.org/core/4_1_0/analyzers-common/org/apache/lucene/analysis/hi/package-summary.html.

  17. 17.

    GATE, http://gate.ac.uk/.

  18. 18.

    QANUS, http://www.qanus.com/.

  19. 19.

    True Knowledge, http://www.evi.com/.

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Authors and Affiliations

  1. Department of IT, Al Khawarizmi International College, Al Ain, UAE

    Santosh Kumar Ray

  2. College of Information Technology, UAE University, Al Ain, UAE

    Amir Ahmad

  3. Faculty of Engineering & IT, The British University in Dubai, Dubai, UAE

    Khaled Shaalan

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  1. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  2. Faculty of Computers and Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Faculty of Computers and Information, Ain Shams University, Cairo, Egypt

    Fahmy Tolba

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Ray, S.K., Ahmad, A., Shaalan, K. (2018). A Review of the State of the Art in Hindi Question Answering Systems. In: Shaalan, K., Hassanien, A., Tolba, F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-319-67056-0_14

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