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Classification of citation sentence for filtering scientific references
Rachman G.H.a, Khodra M.L.a, Widyantoro D.H.a
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics, Bandung, 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]© 2019 IEEE.Citation sentence is able to inform readers about relation between scientific articles that cite and are cited by finding its purpose against the research. Besides giving credit to other researchers and recommendation to read other related articles, citation can help readers to know what knowledge they have obtained based on the cited scientific articles they have read. In this research, we try to define citation categories for filtering scientific references which will be initial step in guided summarization of scientific articles. Our goal is to classify citation sentence first into ‘problem’, ‘other’, ‘useModel’, ‘useTool’ and ‘useData’. This category will make it easier to classify scientific articles into more specific topics. Then we use features namely voice, tenses, citation location, meta-discourse and bag of words. Then, we employ SVM Linear for building classification model and sampling technique, namely SMOTE for imbalance dataset. The best result of f-measure for our citation classification is achieved at 61.2% when combining voice tense, meta-discourse, bag of words and sampling the feature data of UseData category with SMOTE.[/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]Bag of words,Classification models,F measure,Feature data,Sampling technique,Scientific articles,Scientific references,Use-model[/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]Citation sentence,Classification,Filtering scientific references,Scientific article[/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]ACKNOWLEDGMENT This research was partially supported by the Master’s Program toward Doctoral Degree for Excellent Graduate (Program Pendidikan Magister Menuju Doktor untuk Sarjana Unggul/PMDSU) from Kemenristekdikti Indonesia within research entitled “Automatic Guided Summarization”.[/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/10.1109/ICITISEE48480.2019.9003736[/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]