Text mining by Tsallis entropy
Long-range correlations between the elements of natural languages enable them to convey very complex information. Complex structure of human language, as a manifestation of natural languages, motivates us to apply nonextensive statistical mechanics in text mining. Tsallis entropy appropriately ranks...
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| Published in | Physica A Vol. 490; pp. 1368 - 1376 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
15.01.2018
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Long-range correlations between the elements of natural languages enable them to convey very complex information. Complex structure of human language, as a manifestation of natural languages, motivates us to apply nonextensive statistical mechanics in text mining. Tsallis entropy appropriately ranks the terms’ relevance to document subject, taking advantage of their spatial correlation length. We apply this statistical concept as a new powerful word ranking metric in order to extract keywords of a single document. We carry out an experimental evaluation, which shows capability of the presented method in keyword extraction. We find that, Tsallis entropy has reliable word ranking performance, at the same level of the best previous ranking methods.
•Non-extensive statistical mechanics appropriately describes complex systems.•We apply Tsallis entropy to ranks the terms’ relevance to document subject.•Experimental evaluations confirm the capability of this metric in keyword detection.•This metric has reliable word ranking results in comparison with previous methods. |
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| AbstractList | Long-range correlations between the elements of natural languages enable them to convey very complex information. Complex structure of human language, as a manifestation of natural languages, motivates us to apply nonextensive statistical mechanics in text mining. Tsallis entropy appropriately ranks the terms’ relevance to document subject, taking advantage of their spatial correlation length. We apply this statistical concept as a new powerful word ranking metric in order to extract keywords of a single document. We carry out an experimental evaluation, which shows capability of the presented method in keyword extraction. We find that, Tsallis entropy has reliable word ranking performance, at the same level of the best previous ranking methods.
•Non-extensive statistical mechanics appropriately describes complex systems.•We apply Tsallis entropy to ranks the terms’ relevance to document subject.•Experimental evaluations confirm the capability of this metric in keyword detection.•This metric has reliable word ranking results in comparison with previous methods. |
| Author | Mehri, Ali Jamaati, Maryam |
| Author_xml | – sequence: 1 givenname: Maryam surname: Jamaati fullname: Jamaati, Maryam organization: Department of Physics, Iran University of Science and Technology, Narmak 16846-13114, Tehran, Iran – sequence: 2 givenname: Ali surname: Mehri fullname: Mehri, Ali email: alimehri@nit.ac.ir organization: Department of Physics, Faculty of Science, Babol Noshirvani University of Technology, Babol 47148-71167, Iran |
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| CitedBy_id | crossref_primary_10_1007_s12648_022_02463_4 crossref_primary_10_1016_j_physa_2021_126744 crossref_primary_10_1016_j_chaos_2021_110679 crossref_primary_10_1016_j_physa_2019_01_080 crossref_primary_10_1016_j_physa_2020_125273 crossref_primary_10_3390_e20050347 crossref_primary_10_1016_j_physa_2019_121728 crossref_primary_10_1140_epjp_s13360_023_04764_x crossref_primary_10_3389_fnhum_2019_00372 crossref_primary_10_1002_smr_2417 |
| Cites_doi | 10.1007/11671299_27 10.1016/j.eswa.2016.03.045 10.1023/A:1009976227802 10.3115/1119355.1119383 10.1007/BF01016429 10.1016/j.physa.2011.04.013 10.1103/PhysRevE.83.056106 10.1016/j.physleta.2015.04.030 10.1007/978-3-540-77046-6_62 10.3390/e12122497 10.21528/LNLM-vol12-no1-art2 10.1209/epl/i2002-00528-3 10.1147/rd.22.0159 10.1103/PhysRevE.94.052302 10.1103/PhysRev.106.620 10.1016/j.ipm.2007.01.015 10.1016/S0378-4371(03)00625-3 10.1103/PhysRevLett.84.2770 10.1140/epjb/e2016-70423-9 10.1016/j.physa.2013.05.052 10.1016/j.physa.2012.11.052 10.1140/epjb/e2008-00206-x |
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| References | Herrera, Pury (b18) 2008; 63 Carpena, Bernaola-Galván, Carretero-Campos, Coronado (b12) 2016; 94 (b23) 2004 Mehri, Mohammadpour Lashkari (b28) 2016; 89 Siddiqi, Sharan (b4) 2015; 109 Rossi, Maracini, Rezende (b5) 2014; 12 (b1) 2003 Mezard, Montanari (b17) 2009 Mehri, Darooneh (b19) 2011; 390 Tsallis (b26) 2009 Yang, Lei, Fan, Lai (b20) 2013; 392 Jaynes (b22) 1957; 106 Carretero-Campos, Bernaola-Galván, Coronado, Carpena (b30) 2013; 392 Turney (b7) 2000; 2 Weiss, Indurkhya, Zhang, Damerau (b32) 2010 Manning, Schütze (b29) 1999 Mehri, Darooneh (b21) 2011; 83 Onan, Korukoğlu, Bulut (b3) 2016; 57 Wilk, Wlodarczyk (b27) 2000; 84 Palshikar (b13) 2007; 4815 Ortuño, Carpena, Bernaola-Galván, Muñoz, Somoza (b11) 2002; 57 Tsallis (b24) 1988; 52 Berry, Kogan (b6) 2010 . Darooneh, Naeimi, Mehri, Sadeghi (b25) 2010; 12 Luhn (b10) 1958; 2 A. Hulth, Improved automatic keyword extraction given more linguistic knowledge, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Morristown, NJ, USA 10, 2003, pp. 216–223. R. Mihalcea, Random Walks on Text Structures, Gelbukh, A. (Ed.), in: LNCS, CICLing, Springer, Heidelberg, pp. 249–262. Zhou, Slater (b15) 2003; 329 Mehri, Jamaati, Mehri (b16) 2015; 379 Ercan, Cicekli (b9) 2007; 43 Beliga, Meštrović, Martinčić-Ipšić (b2) 2015; 39 Beliga (10.1016/j.physa.2017.09.020_b2) 2015; 39 (10.1016/j.physa.2017.09.020_b23) 2004 Darooneh (10.1016/j.physa.2017.09.020_b25) 2010; 12 Mehri (10.1016/j.physa.2017.09.020_b19) 2011; 390 Jaynes (10.1016/j.physa.2017.09.020_b22) 1957; 106 Manning (10.1016/j.physa.2017.09.020_b29) 1999 Carretero-Campos (10.1016/j.physa.2017.09.020_b30) 2013; 392 Herrera (10.1016/j.physa.2017.09.020_b18) 2008; 63 Carpena (10.1016/j.physa.2017.09.020_b12) 2016; 94 10.1016/j.physa.2017.09.020_b8 Berry (10.1016/j.physa.2017.09.020_b6) 2010 Weiss (10.1016/j.physa.2017.09.020_b32) 2010 Onan (10.1016/j.physa.2017.09.020_b3) 2016; 57 Ortuño (10.1016/j.physa.2017.09.020_b11) 2002; 57 Ercan (10.1016/j.physa.2017.09.020_b9) 2007; 43 Mehri (10.1016/j.physa.2017.09.020_b21) 2011; 83 Tsallis (10.1016/j.physa.2017.09.020_b24) 1988; 52 Turney (10.1016/j.physa.2017.09.020_b7) 2000; 2 Mehri (10.1016/j.physa.2017.09.020_b28) 2016; 89 10.1016/j.physa.2017.09.020_b14 Luhn (10.1016/j.physa.2017.09.020_b10) 1958; 2 10.1016/j.physa.2017.09.020_b31 Mehri (10.1016/j.physa.2017.09.020_b16) 2015; 379 Mezard (10.1016/j.physa.2017.09.020_b17) 2009 Rossi (10.1016/j.physa.2017.09.020_b5) 2014; 12 Zhou (10.1016/j.physa.2017.09.020_b15) 2003; 329 Wilk (10.1016/j.physa.2017.09.020_b27) 2000; 84 Siddiqi (10.1016/j.physa.2017.09.020_b4) 2015; 109 Palshikar (10.1016/j.physa.2017.09.020_b13) 2007; 4815 Tsallis (10.1016/j.physa.2017.09.020_b26) 2009 (10.1016/j.physa.2017.09.020_b1) 2003 Yang (10.1016/j.physa.2017.09.020_b20) 2013; 392 |
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