sonam soni | NMIMS University (original) (raw)
Papers by sonam soni
Journal of Advanced Research in Dynamical and Control Systems, 2019
SSRN Electronic Journal, 2019
Today's educational systems need an efficient tool to perform competently assessment of students ... more Today's educational systems need an efficient tool to perform competently assessment of students on their major concepts they learnt from study material. Preparing a set of questions for assessment can be time consuming for teachers while getting questions from external sources like assessment books or question bank might not be relevant to content studied by students. Automatic Question Generation (AQG) is the technique for generating a right set of questions from a content, which can be text. Automatic question generation (QG) is a very important yet challenging problem in NLP. It is defined as the task of generating syntactically sound, semantically correct and relevant questions from several input formats like text, a structured database or a knowledge base. Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. AQG has the got the immense attention from researchers in a field of computational linguistics. The review paper focuses on the recants ongoing research on NLP for generating automatic questions from the text through various methods. http://ssrn.com/link/ICAESMT-2019.html=xyz Information Systems &eBusiness Network (ISN) Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. Despite its usefulness, manually creating meaningful and relevant questions is a timeconsuming and challenging task. For example, while evaluating students on reading comprehension, it is tedious for a teacher to manually create questions, find answers to those questions, and thereafter evaluate answers. Traditional approaches have either used a linguistically motivated set of transformation rules for transforming a given sentence into a question or a set of manually created templates with slot fillers to generate questions. Recently, neural network-based techniques such as sequence-to-sequence (Seq2Seq) learning have achieved remarkable success in various NLP tasks, including question generation. A modern approach that is deep learning is used to generates question. The approach proposed by author explore a straightforward task of question generation only from a triplet of subject, relation and object. Sequence-to-sequence prototype with attention for question generation from passages. The proposed algorithm generates questions and answers from corpus using pointer networks.
Emerging Technologies in Data Mining and Information Security, 2021
Journal of Advanced Research in Dynamical and Control Systems, 2019
SSRN Electronic Journal, 2019
Today's educational systems need an efficient tool to perform competently assessment of students ... more Today's educational systems need an efficient tool to perform competently assessment of students on their major concepts they learnt from study material. Preparing a set of questions for assessment can be time consuming for teachers while getting questions from external sources like assessment books or question bank might not be relevant to content studied by students. Automatic Question Generation (AQG) is the technique for generating a right set of questions from a content, which can be text. Automatic question generation (QG) is a very important yet challenging problem in NLP. It is defined as the task of generating syntactically sound, semantically correct and relevant questions from several input formats like text, a structured database or a knowledge base. Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. AQG has the got the immense attention from researchers in a field of computational linguistics. The review paper focuses on the recants ongoing research on NLP for generating automatic questions from the text through various methods. http://ssrn.com/link/ICAESMT-2019.html=xyz Information Systems &eBusiness Network (ISN) Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. Despite its usefulness, manually creating meaningful and relevant questions is a timeconsuming and challenging task. For example, while evaluating students on reading comprehension, it is tedious for a teacher to manually create questions, find answers to those questions, and thereafter evaluate answers. Traditional approaches have either used a linguistically motivated set of transformation rules for transforming a given sentence into a question or a set of manually created templates with slot fillers to generate questions. Recently, neural network-based techniques such as sequence-to-sequence (Seq2Seq) learning have achieved remarkable success in various NLP tasks, including question generation. A modern approach that is deep learning is used to generates question. The approach proposed by author explore a straightforward task of question generation only from a triplet of subject, relation and object. Sequence-to-sequence prototype with attention for question generation from passages. The proposed algorithm generates questions and answers from corpus using pointer networks.
Emerging Technologies in Data Mining and Information Security, 2021