NLP References

Материал из Wiki - Факультет компьютерных наук
Перейти к: навигация, поиск
  1. Salton, Gerard, and Christopher Buckley. Term-weighting approaches in automatic text retrieval. Information processing and management 24.5 (1988): 513-523.
  2. Wong, SK Michael, Wojciech Ziarko, and Patrick CN Wong. Generalized vector spaces model in information retrieval. Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 1985.
  3. Turney, Peter D., and Patrick Pantel. From frequency to meaning: Vector space models of semantics. Journal of artificial intelligence research 37.1 (2010): 141-188.
  4. Ponte, Jay M., and W. Bruce Croft. A language modeling approach to information retrieval. Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 1998.
  5. Zamir, Oren, and Oren Etzioni. Web document clustering: A feasibility demonstration. Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 1998.
  6. Kennington, Casey Redd, Martin Kay, and Annemarie Friedrich. Suffix Trees as Language Models. LREC. 2012.
  7. Huang, Jin Hu, and David Powers. Suffix tree based approach for chinese information retrieval. Intelligent Systems Design and Applications, 2008. ISDA'08. Eighth International Conference on. Vol. 3. IEEE, 2008.
  8. Zhang, Dell, and Wee Sun Lee. Extracting key-substring-group features for text classification. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.
  9. Gusfield, Dan. Algorithms on strings, trees and sequences: computer science and computational biology. Cambridge university press, 1997.
  10. Pampapathi R., Mirkin B., Levene M., A suffix tree approach to anti-spam email filtering, Machine Learning, 2006, Vol. 65, no.1, pp. 309-338.
  11. Chernyak E.L., Chugunova O.N., Mirkin B.G., Annotated suffix tree method for measuring degree of string to text belongingness, Business Informatics, 2012. Vol. 21, no.3, pp. 31-41 (in Russian).
  12. Chernyak E.L., Chugunova O.N., Askarova J.A., Nascimento S., Mirkin B.G., Abstracting concepts from text documents by using an ontology, in Proceedings of the 1st International Workshop on Concept Discovery in Unstructured Data. 2011, pp. 21-31.
  13. Chernyak E. L. An approach to the problem of annotation of research publications, Proceedings of The Eighth International Conference on Web Search and Data Mining, pp. 429-434.
  14. Chernyak E. L., Mirkin B. G. Refining a Taxonomy by Using Annotated Suffix Trees and Wikipedia Resources. Annals of Data Science. Vol. 2. No. 1. P. 61-82, 2015.
  15. Morenko, E. N., Chernyak E.L., Mirkin B.G.. Conceptual Maps: Construction Over a Text Collection and Analysis. In Analysis of Images, Social Networks and Texts, pp. 163-168. Springer International Publishing, 2014.
  16. Martin, James H., and Daniel Jurafsky. "Speech and language processing." International Edition (2000).
  17. Manning, Christopher D., and Hinrich Schuetze. Foundations of statistical natural language processing. MIT press, 1999.
  18. Santini, Marina, and Serge Sharoff. Web Genre Benchmark Under Construction. JLCL, Volume 24 (1), 2009.
  19. Luhn, Hans Peter. "A statistical approach to mechanized encoding and searching of literary information." IBM Journal of research and development 1.4 (1957): 309-317.
  20. Sparck Jones, Karen. "A statistical interpretation of term specificity and its application in retrieval." Journal of documentation 28.1 (1972): 11-21.
  21. Sebastiani, Fabrizio. "Machine learning in automated text categorization." ACM computing surveys (CSUR) 34.1 (2002): 1-47.
  22. Salton, Gerard, Anita Wong, and Chung-Shu Yang. "A vector space model for automatic indexing." Communications of the ACM 18.11 (1975): 613-620.
  23. Bird, Steven, Ewan Klein, and Edward Loper. Natural language processing with Python. " O'Reilly Media, Inc.", 2009.