TY - JOUR
T1 - Question classification and answer extraction for developing a cooking QA system
AU - Rahman Khilji, Abdullah Faiz Ur
AU - Manna, Riyanka
AU - Laskar, Sahinur Rahman
AU - Pakray, Partha
AU - Das, Dipankar
AU - Bandyopadhyay, Sivaji
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© 2020 Instituto Politecnico Nacional. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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%.
AB - 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%.
KW - Answer extraction
KW - BERT
KW - Cooking QA
KW - Question classification
UR - http://www.scopus.com/inward/record.url?scp=85094219364&partnerID=8YFLogxK
U2 - 10.13053/CyS-24-2-3445
DO - 10.13053/CyS-24-2-3445
M3 - Artículo
AN - SCOPUS:85094219364
SN - 1405-5546
VL - 24
SP - 921
EP - 927
JO - Computacion y Sistemas
JF - Computacion y Sistemas
IS - 2
ER -