Soft similarity and soft cosine measure: Similarity of features in vector space model

Computacion y Sistemas

Volumn 18, Issue 3, 2014, Pages 491-504

  Sidorov, Grigori ^a   Gelbukh, Alexander ^a   Gómez Adorno, Helena ^a   Pinto, David ^b  

^a INSTITUTO POLITÉCNICO NACIONAL   (Mexico)

^b BENEMÉRITA UNIVERSIDAD AUTÓNOMA DE PUEBLA   (Mexico)

Author keywords

Levenshtein distance; N grams; Similarity between features; Soft cosine measure; Soft similarity; Syntactic n grams; Vector space model

Indexed keywords

EID: 84907532699     PISSN: 14055546     EISSN: 20079737     Source Type: Journal    
DOI: 10.13053/CyS-18-3-2043     Document Type: Article

References (21)

1. Bejar, I., Chaffin, R., & Embretson, S. (1991). Cognitive and psychometric analysis of analogical problem solving. Recent research in psychology. Springer-Verlag.
2. Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische mathematik, 1(1), 269-271.
3. Gmez-Adorno, H., Sidorov, G., Pinto, D., & Gelbukh, A. (2014). Graph-based approach to the question answering task based on entrance exams. Cappellato, L., Ferro, N., Halvey, M., & Kraaij, W., editors, Notebook for PAN at CLEF 2014. CLEF 2014. CLEF2014 Working Notes, volume 1180 of CEUR Workshop Proceedings, CEUR-WS.org, pp. 1395-1403.
4. Jimenez, S., Gonzalez, F., & Gelbukh, A. (2010). Text comparison using soft cardinality. Chavez, E. & Lonardi, S., editors, String Processing and Information Retrieval, volume 6393 of Lecture Notes in Computer Science, Springer, pp. 297-302.
5. Jimenez Vargas, S. & Gelbukh, A. (2012). Baselines for natural language processing tasks based on soft cardinality spectra. International Journal of Applied and Computational Mathematics, 11(2), 180-199.
6. Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10(8), 707-710.
7. Li, B. & Han, L. (2013). Distance weighted cosine similarity measure for text classification. Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., & Yao, X., editors, IDEAL, volume 8206 of Lecture Notes in Computer Science, Springer, pp. 611-618.
8. Mikawa, K., Ishida, T., & Goto, M. (2011). A proposal of extended cosine measure for distance metric learning in text classification. Systems, Man, and Cybernetics (SMC), IEEE, pp. 1741-1746.
9. Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38, 39-41.
10. Penãs, A., Hovy, E. H., Forner, P., Rodrigo, Á., Sutcliffe, R. F. E., Forascu, C., & Sporleder, C. (2011). Overview of qa4mre at clef 2011: Question answering for machine reading evaluation. CLEF (Notebook Papers/Labs/Workshop), pp. 1-20.
11. Penãs, A., Hovy, E. H., Forner, P., Rodrigo, Á., Sutcliffe, R. F. E., Sporleder, C., Forascu, C., Benajiba, Y., & Osenova, P. (2012). Overview of qa4mre at clef 2012: Question answering for machine reading evaluation. CLEF (Online Working Notes/Labs/Workshop), pp. 1-24.
12. Peñas, A., Miyao, Y., Forner, P., & Kando, N. (2013). Overview of qa4mre 2013 entrance exams task. CLEF (Online Working Notes/Labs/Workshop), pp. 1-6.
13. Pinto, D., Gómez-Adorno, H., Ayala, D. V., & Singh, V. K. (2014). A graph-based multi-level linguistic representation for document understanding. Pattern Recognition Letters, 41, 93-102.
14. Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., & Howard, N. (2014). Dependency-based semantic parsing for concept-level text analysis. 15th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2014, Part I, number 8403 in Lecture Notes in Computer Science, Springer, pp. 113-127.
15. Poria, S., Gelbukh, A., Cambria, E., Hussain, A., & Huang, G.-B. (2015). EmoSenticSpace: A novel framework for affective common-sense reasoning. Knowledge-Based Systems.
16. Poria, S., Gelbukh, A., Hussain, A., Howard, N., Das, D., & Bandyopadhyay, S. (2013). Enhanced SenticNet with affective labels for concept-based opinion mining. IEEE Intelligent Systems, 28, 31-38.
17. Salton, G., editor (1988). Automatic text processing. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
18. Sanchez-Perez, M., Sidorov, G., & Gelbukh, A. (2014). The winning approach to text alignment for text reuse detection at pan 2014. Cappellato, L., Ferro, N., Halvey, M., & Kraaij, W., editors, Notebook for PAN at CLEF 2014. CLEF 2014. CLEF2014 Working Notes, volume 1180 of CEUR Workshop Proceedings, CEUR-WS.org, pp. 1004-1011.
19. Sidorov, G. (2013). Syntactic dependency based n-grams in rule based automatic English as second language grammar correction. International Journal of Computational Linguistics and Applications, 4(2), 169-188. Methods and Applications of Artificial and Computational Intelligence.
20. Sidorov, G. (2014). Should syntactic n-grams contain names of syntactic relations? International Journal of Computational Linguistics and Applications, 5(1), 139-158.
21. Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., & Chanona-Hernndez, L. (2014). Syntactic n-grams as machine learning features for natural language processing. Expert Systems with Applications, 41(3), 853-860. Methods and Applications of Artificial and Computational Intelligence.