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

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. 1.

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

  2. 2.

    https://tac.nist.gov/.

  3. 3.

    https://www.nist.gov/.

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 11.

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

  12. 12.

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

  13. 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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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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Keywords

  • Scientific Text Entailment
  • cooking domain question answering
  • Long Short-Term Memory neural networks
  • Support Vector Machine