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Extend relation identification in scientific papers based on supervised machine learning

Sibaroni Y.a, Widyantoro D.H.a, Khodra M.L.a

a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia, 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 IEEE.This paper discusses the identification of extend relation in scientific papers based on supervised machine learning. Identification of extend relations is conducted by classifying each sentence in scientific papers into extend category. Extend relation is one type of papers’ relations that obtained by using the citation context based approach. Citation context is a set of words or phrases in a sentence collection that citing or discussing other papers. Citation context based approach use the semantic of citation sentence to identify the relationship between scientific papers and can identify more varied relationship than two other approaches i.e. content-based and citation analysis. The recently research in papers’ relations used a rule-based approach to identify extend relations. In this paper, supervised learning approached with proposed features set was used to identify extend relations. The learning of classifier model is explored by using Naïve Bayes, Decision Tree, Ibk and Logistic Regression. Experimental results show that the performance of extend sentence classification based on supervised machine learning with proposed features is superior compared to the baseline. Feature selection based on correlation value is also effective to improve the performance of the extend sentence classification.[/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]Citation contexts,Extend,Feature,Relations,Rule based,Supervised machine learning[/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 Context,Extend,Feature,Relations,Rule-based,Supervised machine Learning[/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/10.1109/ICACSIS.2016.7872724[/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]