TY - GEN

T1 - Stop-words in keyphrase extraction problem

AU - Popova, S.

AU - Kovriguina, L.

AU - Khodyrev, I.

AU - Mouromtsev, D.

PY - 2013

Y1 - 2013

N2 - Abstract—Keyword extraction problem is one of the most significant tasks in information retrieval. High-quality keyword extraction sufficiently influences the progress in the following subtasks of information retrieval: classification and clustering, data mining, knowledge extraction and representation, etc. The research nvironment has specified a layout for keyphrase extraction. However, some of the possible decisions remain uninvolved in the paradigm. In the paper the authors observe the scope of interdisciplinary methods applicable to automatic stop list feeding. The chosen method belongs to the class of experiential models. The research procedure based on this method allows to improve the quality of keyphrase extraction on the stage of candidate keyphrase building. Several ways to automatic feeding of the stop lists are proposed in the paper as well. One of them is based on provisions of lexical statistics and the results of its application to the discussed task point out the non-gaussian nature of text c

AB - Abstract—Keyword extraction problem is one of the most significant tasks in information retrieval. High-quality keyword extraction sufficiently influences the progress in the following subtasks of information retrieval: classification and clustering, data mining, knowledge extraction and representation, etc. The research nvironment has specified a layout for keyphrase extraction. However, some of the possible decisions remain uninvolved in the paradigm. In the paper the authors observe the scope of interdisciplinary methods applicable to automatic stop list feeding. The chosen method belongs to the class of experiential models. The research procedure based on this method allows to improve the quality of keyphrase extraction on the stage of candidate keyphrase building. Several ways to automatic feeding of the stop lists are proposed in the paper as well. One of them is based on provisions of lexical statistics and the results of its application to the discussed task point out the non-gaussian nature of text c

U2 - 10.1109/FRUCT.2013.6737953

DO - 10.1109/FRUCT.2013.6737953

M3 - Conference contribution

SP - 113

EP - 121

BT - 14th Conference of Open Innovations Association (FRUCT), 2013

PB - Institute of Electrical and Electronics Engineers Inc.

ER -