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      https://www.ias.ac.in/article/fulltext/sadh/046/0024

    • Keywords

       

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

    • 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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    • Author Affiliations

       

      AMARNATH PATHAK1 RIYANKA MANNA2 PARTHA PAKRAY3 DIPANKAR DAS2 ALEXANDER GELBUKH4 SIVAJI BANDYOPADHYAY3

      1. Department of CSE, National Institute of Technology Mizoram, Aizawl, India
      2. Department of CSE, Jadavpur University, Kolkata, India
      3. Department of CSE, National Institute of Technology Silchar, Silchar, India
      4. Centro de Investigacion en Computacion (CIC), Instituto Politecnico Nacional (IPN), Mexico City, Mexico
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