Question Classification and Answer Extraction for Developing a Cooking QA System
Título del documento: Question Classification and Answer Extraction for Developing a Cooking QA System
Revue: Computación y sistemas
Base de datos:
Número de sistema: 000560506
ISSN: 1405-5546
Autores: 1
2
3
1
2
1
3
Instituciones: 1National Institute of Technology Silchar, Department of Computer Science and Engineering, Assam. India
2Jadavpur University, Department of Computer Science and Engineering, Kolkata, West Bengal. India
3Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City. México
Año:
Periodo: Abr-Jun
Volumen: 24
Número: 2
Paginación: 927-933
País: México
Idioma: Inglés
Tipo de documento: Artículo
Resumen en inglés In an automated Question Answering (QA) system, Question Classification (QC) is an essential module. The aim of QC is to identify the type of questions and classify them based on the expected answer type. Although the machine-learning approach overcomes the limitation of rules as is the case with the conventional rule-based approach but is restricted to the predefined class of questions. The existing approaches are too specific for the users. To address this challenge, we have developed a cooking QA system in which a recipe question is contextually classified into a particular category using deep learning techniques. The question class is then used to extract the requisite details from the recipe obtained via the rule-based approach to provide a precise answer. The main contribution of this paper is the description of the QC module of the cooking QA system. The obtained intermediate classification accuracy over the unseen data is 90% and the human evaluation accuracy of the final system output is 39.33%.
Disciplinas: Ciencias de la computación
Palabras clave: Inteligencia artificial
Keyword: Question classification,
Answer extraction,
Cooking QA,
BERT,
Artificial intelligence
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