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Pattern discovery using QG for question-answering pairs
Suwarningsih W.a,b, Supriana I.a, Purwarianti A.a
a School of Electronic Engineering and Informatics, Bandung Institute of Technology, Indonesia
b Research Center for Informatics, Indonesian Institute of Science, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2016, School of Electrical Engineering and Informatics. All rights reserved.The hardest things in developing the question-answer system are to raise a question that comes from natural language sentences and to find the answers to some questions relevant to the query. In this paper, the strategy to be developed is how to apply a natural language processing using a technique automatically to generate questions and answers. A number of new ideas have been explored including a semantic-based template using a combination of semantic role labeling (SRL) with the predicate argument (PA) to create a semantic pattern within the scope of medical Indonesian sentences. It was more focused on Question Generating (QG) with a discourse task involving the following three steps: (1) Parsing the labeling of semantic-based element PICO with progression to PPPICCOODTQ (Problem, Patient, Intervention, Compare, Control, Outcome, Organs, Drug, Time, Quantity); (2) Identification and Transformation sentence; and (3) Filtering for answering Question construction. This study has presented a new approach by utilizing the semantic role labeling and flexibility template. This approach achieved the accuracy values of 0.80 simple sentence. The results showed the improvement of the performance of question generation from the information on medical outcomes.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Medical question generating,PPPICCOODTQ element,Question-answering pairs,Sentences transformation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.15676/ijeei.2016.8.2.1[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]