Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:S...Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:Semantic dictionary and conditional random field model(CRFM)were used to annotate the semantic information of research papers.Based on the annotation results,the research level information was extracted through regular expression.All the functions were implemented on Sybase platform.Findings:According to the result of our experiment in carbon nanotube research,the precision and recall rates reached 65.13%and 57.75%,respectively after the semantic properties of word class have been labeled,and F-measure increased dramatically from less than 50%to60.18%while added with semantic features.Our experiment also showed that the information extraction system for research level(IESRL)can extract performance indicators from research papers rapidly and effectively.Research limitations:Some text information,such as that of format and chart,might have been lost due to the extraction processing of text format from PDF to TXT files.Semantic labeling on sentences could be insufficient due to the rich meaning of lexicons in the semantic dictionary.Research implications:The established system can help researchers rapidly compare the level of different research papers and find out their implicit innovation values.It could also be used as an auxiliary tool for analyzing research levels of various research institutions.Originality/value:In this work,we have successfully established an information extraction system for research papers by a revised semantic annotation method based on CRFM and the semantic dictionary.Our system can analyze the information extraction problem from two levels,i.e.from the sentence level and noun(phrase)level of research papers.Compared with the extraction method based on knowledge engineering and that on machine learning,our system shows advantages of the both.展开更多
基金supported by the National Social Science Foundation of China(Grant No.12CTQ032)
文摘Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:Semantic dictionary and conditional random field model(CRFM)were used to annotate the semantic information of research papers.Based on the annotation results,the research level information was extracted through regular expression.All the functions were implemented on Sybase platform.Findings:According to the result of our experiment in carbon nanotube research,the precision and recall rates reached 65.13%and 57.75%,respectively after the semantic properties of word class have been labeled,and F-measure increased dramatically from less than 50%to60.18%while added with semantic features.Our experiment also showed that the information extraction system for research level(IESRL)can extract performance indicators from research papers rapidly and effectively.Research limitations:Some text information,such as that of format and chart,might have been lost due to the extraction processing of text format from PDF to TXT files.Semantic labeling on sentences could be insufficient due to the rich meaning of lexicons in the semantic dictionary.Research implications:The established system can help researchers rapidly compare the level of different research papers and find out their implicit innovation values.It could also be used as an auxiliary tool for analyzing research levels of various research institutions.Originality/value:In this work,we have successfully established an information extraction system for research papers by a revised semantic annotation method based on CRFM and the semantic dictionary.Our system can analyze the information extraction problem from two levels,i.e.from the sentence level and noun(phrase)level of research papers.Compared with the extraction method based on knowledge engineering and that on machine learning,our system shows advantages of the both.
