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IESRL:An information extraction system for research level

Chinese Journal of Library and Information Science 2013年第4期6卷 16-27页

作者： Fuhai LENG Rujiang BAI Qingsong ZHU National Science Library Beijing HeadquartersChinese Academy of Sciences Library of Shandong University of Technology

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 ***/methodology/approach:Semantic dictionary and conditional random field model(CRFM)were used to annotate the semantic information of research *** on the annotation results,the research level information was extracted through regular *** the functions were implemented on Sybase ***: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 *** experiment also showed that the information extraction system for research level(IESRL)can extract performance indicators from research papers rapidly and *** 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 *** labeling on sentences could be insufficient due to the rich meaning of lexicons in the semantic *** implications:The established system can help researchers rapidly compare the level of different research papers and find out their implicit innovation *** could also be used as an auxiliary tool for analyzing research levels of various research ***/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 *** system can analyze the information extraction problem from two levels,*** the sentence level and noun(phrase)level of research *** with the extraction method based on knowledge engineering and that on machine learning,our system shows advantages

关键词： research papers Information extraction Semantic labeling Regular expression Conditional random fields research level

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