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Discovery indonesian medical question-answering pairs pattern with question generation
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]© Research India Publications.Question generating refers to the task of automatically generating questions from various inputs such as raw text, database, or semantic representation. This paperconcerns with the development of the novel question generating methods for Indonesian medical question answering. It begins by presenting an overview of the main methods and followed by showing the architecture of the proposed system in detail. Furthermore, this paper proposed a novel template-based approach to question a generation by combining a number of semantic roles through a method of generating both general and domain-specific questions. It was more focused on Question Generating with a discourse task involving the following three steps: (1) content selection of sentence (Problem, Intervention, Comparison and Outcome (PICO) categories), (2) question type identification, and (3) question answering construction. It is argued then that many ways in which the sentences of the answer to a question can be formulated and acquired from resources containing a large number of semantically related sentences. This approach achievedF-measure values of 0.88, precision 0,84 and recall 0,86 for active sentence. Finally, it can be concluded that the pattern matching criteria of the training set and semantic role labelling based on PICO frame can be reproducible with minimal expert intervention.[/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,PICO frame,Question-answering pairs,Semantic 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][/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]