• SIVAJI BANDYOPADHYAY

      Articles written in Sadhana

    • Classifier combination approach for question classification for Bengali question answering system

      SOMNATH BANERJEE SUDIP KUMAR NASKAR PAOLO ROSSO SIVAJI BANDYOPADHYAY

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      Question classification (QC) is a prime constituent of an automated question answering system. The work presented here demonstrates that a combination of multiple models achieves better classification performance than those obtained with existing individual models for the QC task in Bengali. We have exploited stateof-the-art multiple model combination techniques, i.e., ensemble, stacking and voting, to increase QC accuracy. Lexical, syntactic and semantic features of Bengali questions are used for four well-known classifiers, namelyNaĩve Bayes, kernel Naı¨ve Bayes, Rule Induction and Decision Tree, which serve as our base learners. Singlelayer question-class taxonomy with 8 coarse-grained classes is extended to two-layer taxonomy by adding 69 fine-grained classes. We carried out the experiments both on single-layer and two-layer taxonomies. Experimental results confirmed that classifier combination approaches outperform single-classifier classification approaches by 4.02% for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.

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

      AMARNATH PATHAK RIYANKA MANNA PARTHA PAKRAY DIPANKAR DAS ALEXANDER GELBUKH SIVAJI BANDYOPADHYAY

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      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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