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Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
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doi: 10.17586/2226-1494-2017-17-1-81-91
METHOD OF RARE TERM CONTRASTIVE EXTRACTION FROM NATURAL LANGUAGE TEXTS
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Article in Russian
For citation: Bessmertny I.A., Nugumanova A.B., Mansurova M.Ye., Baiburin Ye.M. Method of rare term contrastive extraction from natural language texts. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 1, pp. 81–91. doi: 10.17586/2226-1494-2017-17-1-81-91
Abstract
For citation: Bessmertny I.A., Nugumanova A.B., Mansurova M.Ye., Baiburin Ye.M. Method of rare term contrastive extraction from natural language texts. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 1, pp. 81–91. doi: 10.17586/2226-1494-2017-17-1-81-91
Abstract
The paper considers a problem of automatic domain term extraction from documents corpus by means of a contrast collection. Existing contrastive methods successfully extract often used terms but mishandle rare terms. This could yield poorness of the resulting thesaurus. Assessment of point-wise mutual information is one of the known statistical methods of term extraction and it finds rare terms successfully. Although, it extracts many false terms at that. The proposed approach consists of point-wise mutual information application for rare terms extraction and filtering of candidates by criterion of joint occurrence with the other candidates. We build “documents-by-terms” matrix that is subjected to singular value decomposition to eliminate noise and reveal strong interconnections. Then we pass on to the resulting matrix “terms-by-terms” that reproduces strength of interconnections between words. This approach was approved on a documents collection from “Geology” domain with the use of contrast documents from such topics as “Politics”, “Culture”, “Economics” and “Accidents” on some Internet resources. The experimental results demonstrate operability of this method for rare terms extraction
Keywords: contrastive term extraction, termhood, mutual information, semantic connections, rare term extraction
Acknowledgements. The paper contains data for study partially financially supported by the Grant 5033/ГФ4 of the Ministry of Education and Science of the Republic of Kazakhstan "The development of intelligent high-performance information and analysis search engine for semistructured data processing"
References
Acknowledgements. The paper contains data for study partially financially supported by the Grant 5033/ГФ4 of the Ministry of Education and Science of the Republic of Kazakhstan "The development of intelligent high-performance information and analysis search engine for semistructured data processing"
References
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