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Scientific Text Entailment and a Textual-Entailment-based framework for cooking domain question answering

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Abstract

Detecting entailment relationship between two sentences has profoundly impacted several different application areas of Natural Language Processing (NLP). Though recognizing textual entailment (TE) is amongst the widely studied problems, the research on detecting entailment between pieces of scientific texts is still in its infancy. To this end the paper discusses implementation of systems based on Long Short-Term Memory (LSTM) neural network and Support Vector Machine (SVM) classifiers using SCITAIL entailment dataset, a dataset in which premise and hypothesis are constituted of scientific texts. Also, a TE-based framework for cooking domain question answering is introduced. The proposed framework exploits the entailment relationship between user question and the cooking questions contained inside a Knowledge Base (KB).

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Notes

  1. http://reverb.cs.washington.edu/.

  2. https://tac.nist.gov/.

  3. https://www.nist.gov/.

  4. http://nlp.cs.illinois.edu/HockenmaierGroup/data.html.

  5. https://www.cs.york.ac.uk/semeval-2012/task6/index.php?id=data.

  6. http://data.allenai.org/scitail/leaderboard/.

  7. https://lucene.apache.org/core/.

  8. https://wordnetcode.princeton.edu/2.1/.

  9. http://svn.ask.it.usyd.edu.au/trac/candc/.

  10. http://nlp.stanford.edu/software/lex-parser.shtml.

  11. https://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  12. https://code.google.com/archive/p/word2vec/.

  13. https://github.com/pathakamarnath/Cooking-Knowledge-Base.

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Acknowledgements

The work presented in this paper falls under Research Project Grant Nos. YSS/2015/000988 (DST-SERB) and partially under Research Project No. IFC/4130/DST-CNRS/2018-19/IT25 (DST-CNRS). The authors would like to acknowledge the Department of Computer Science and Engineering, National Institute of Technology Mizoram, and Department of Computer Science and Engineering, National Institute of Technology Silchar, for providing infrastructural facilities and support.

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Authors and Affiliations

  1. Department of CSE, National Institute of Technology Mizoram, Aizawl, India

    Amarnath Pathak

  2. Department of CSE, Jadavpur University, Kolkata, India

    Riyanka Manna & Dipankar Das

  3. Department of CSE, National Institute of Technology Silchar, Silchar, India

    Partha Pakray & Sivaji Bandyopadhyay

  4. Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Mexico City, Mexico

    Alexander Gelbukh

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  1. Amarnath Pathak
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  2. Riyanka Manna
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  3. Partha Pakray
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  4. Dipankar Das
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  5. Alexander Gelbukh
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  6. Sivaji Bandyopadhyay
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Correspondence to Partha Pakray.

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Pathak, A., Manna, R., Pakray, P. et al. Scientific Text Entailment and a Textual-Entailment-based framework for cooking domain question answering. Sādhanā 46, 24 (2021). https://doi.org/10.1007/s12046-021-01557-9

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