Fitness Terms Assignment

  • 1.

    Chen, Q., Li, M., Zhou, M.: Improving query spelling correction using web search results. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007, pp. 181–189. ACL, Stroudsburg (2007)Google Scholar

  • 2.

    Eisenstein, J., O’Connor, B., Smith, N.A., Xing, E.P.: Mapping the geographical diffusion of new words. In: Workshop on Social Network and Social Media Analysis: Methods, Models and Applications, NIPS 2012 (2012)Google Scholar

  • 3.

    Ntoulas, A., Cho, J., Olston, C.: What’s new on the web?: The evolution of the web from a search engine perspective. In: Proceedings of the 13th International Conference on World Wide Web, WWW 2004, pp. 1–12. ACM, New York (2004)Google Scholar

  • 4.

    Ranganath, P.: From microprocessors to nanostores: Rethinking data-centric systems. IEEE Computer 44(1), 39–48 (2011)CrossRefGoogle Scholar

  • 5.

    Robertson, S., Zaragoza, H.: The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends in Information Retrieval 3(4), 333–389 (2009)CrossRefGoogle Scholar

  • 6.

    Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. Journal of the American Society for Information science 27(3), 129–146 (1976)CrossRefGoogle Scholar

  • 7.

    Rocchio, J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The Smart retrieval system - experiments in automatic document processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)Google Scholar

  • 8.

    Sun, H.M.: A study of the features of internet english from the linguistic perspective. Studies in Literature and Language 1(7), 98–103 (2010)Google Scholar

  • 9.

    Williams, H.E., Zobel, J.: Searchable words on the web. International Journal on Digital Libraries 5(2), 99–105 (2005)CrossRefGoogle Scholar

  • 10.

    Zhu, Y., Zhong, N., Xiong, Y.: Data explosion, data nature and dataology. In: Zhong, N., Li, K., Lu, S., Chen, L. (eds.) BI 2009. LNCS, vol. 5819, pp. 147–158. Springer, Heidelberg (2009)CrossRefGoogle Scholar

  • 1.

    Ranganathan P. From microprocessors to nanostores: rethinking datacentric systems. IEEE Computer, 2011, 44(1): 39–48Google Scholar

  • 2.

    Zhu Y Y, Zhong N, Xiong Y. Data explosion, data nature and dataology. In: Proceedings of International Conference on Brain Informatics. 2009, 147–158Google Scholar

  • 3.

    Ntoulas A, Cho J, Olston C. What’s new on the Web?: the evolution of the Web from a search engine perspective. In: Proceedings of the 13th International Conference on World Wide Web. 2004, 1–12Google Scholar

  • 4.

    Bharat K, Broder A. A technique for measuring the relative size and overlap of public web search engines. Computer Networks and ISDN Systems, 1998, 30(1): 379–388Google Scholar

  • 5.

    Williams H E, Zobel J. Searchable words on the Web. International Journal on Digital Libraries, 2005, 5(2): 99–105Google Scholar

  • 6.

    Eisenstein J, O’Connor B, Smith N A, Xing E P. Mapping the geographical diffusion of new words. In: Proceedings of Workshop on Social Network and Social Media Analysis: Methods, Models and Applications. 2012Google Scholar

  • 7.

    Sun H M. A study of the features of internet english from the linguistic perspective. Studies in Literature and Language, 2010, 1(7): 98–103Google Scholar

  • 8.

    Chen Q, Li M, Zhou M. Improving query spelling correction usingWeb search results. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2007, 181–189Google Scholar

  • 9.

    Subramaniam L V, Roy S, Faruquie T A, Negi S. A survey of types of text noise and techniques to handle noisy text. In: Proceedings of the 3rd Workshop on Analytics for Noisy Unstructured Text Data. 2009, 115–122Google Scholar

  • 10.

    Ahmad F, Kondrak G. Learning a spelling error model from search query logs. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 2005, 955–962Google Scholar

  • 11.

    Carpineto C, Romano G. A survey of automatic query expansion in information retrieval. ACM Computing Surveys, 2012, 44(1): 1–50MATHGoogle Scholar

  • 12.

    Véronis J. Hyperlex: lexical cartography for information retrieval. Computer Speech & Language, 2004, 18(3): 223–252Google Scholar

  • 13.

    Bernardini A, Carpineto C, Amico M D. Full-subtopic retrieval with keyphrase-based search results clustering. In: Proceedings of IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technologies. 2009, 206–213Google Scholar

  • 14.

    Wong S K M, Ziarko W, Raghavan V V, Wong P. On modeling of information retrieval concepts in vector spaces. ACM Transactions on Database Systems, 1987, 12(2): 299–321Google Scholar

  • 15.

    Crestani F. Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 1997, 11(6): 453–482Google Scholar

  • 16.

    Carpineto C, Romano G. Concept Data Analysis: Theory and Applications. Chichester: John Wiley & Sons, 2004MATHGoogle Scholar

  • 17.

    Sahlgren M. An introduction to random indexing. In: Proceedings of Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering. 2005Google Scholar

  • 18.

    Melucci M. A basis for information retrieval in context. ACM Transactions on Information Systems, 2008, 26(3): 1–41Google Scholar

  • 19.

    Sun R, Ong C H, Chua T S. Mining dependency relations for query expansion in passage retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2006, 382–389Google Scholar

  • 20.

    Schlaefer N, Ko J, Betteridge J, Pathak M A, Nyberg E, Sautter G. Semantic extensions of the Ephyra QA system for TREC 2007. In: Proceedings of the 16th Text REtrieval Conference. 2007Google Scholar

  • 21.

    Kraaij W, Nie J Y, Simard M. Embedding Web-based statistical translation models in cross-language information retrieval. Computational Linguistics, 2003, 29(3): 381–419MATHGoogle Scholar

  • 22.

    Kherfi M L, Ziou D, Bernardi A. Image retrieval from the World Wide Web: issues, techniques, and systems. ACM Computing Surveys, 2004, 36(1): 35–67Google Scholar

  • 23.

    Natsev A P, Haubold A, Tešić J, Xie L X, Yan R. Semantic conceptbased query expansion and re-ranking for multimedia retrieval. In: Proceedings of the 15th ACM International Conference on Multimedia. 2007, 991–1000Google Scholar

  • 24.

    Arguello J, Elsas J L, Callan J, Carbonell J G. Document representation and query expansion models for blog recommendation. In: Proceedings of the 2nd International Conference onWeblogs and Social Media. 2008, 10–18Google Scholar

  • 25.

    Hidalgo J M G, de Buenaga Rodríguez M, Pérez J C C. The role of word sense disambiguation in automated text categorization. In: Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems. 2005, 298–309Google Scholar

  • 26.

    Graupmann J, Cai J, Schenkel R. Automatic query refinement using mined semantic relations. In: Proceedings of International Workshop on Challenges in Web Information Retrieval and Integration. 2005, 205–213Google Scholar

  • 27.

    Kamvar M, Baluja S. The role of context in query input: using contextual signals to complete queries on mobile devices. In: Proceedings of the 9th International Conference on Human Computer Interaction with Mobile Devices and Services. 2007, 405–412Google Scholar

  • 28.

    Huang C C, Lin K M, Chien L F. Automatic training corpora acquisition through Web mining. In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technologies. 2005, 193–199Google Scholar

  • 29.

    Perugini S, Ramakrishnan N. Interacting withWeb hierarchies. IT Professional, 2006, 8(4): 19–28Google Scholar

  • 30.

    Church K, Smyth B. Mobile content enrichment. In: Proceedings of the 12th International Conference on Intelligent User Interfaces. 2007, 112–121Google Scholar

  • 31.

    Macdonald C, Ounis I. Expertise drift and query expansion in expert search. In: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management. 2007, 341–350Google Scholar

  • 32.

    Billerbeck B, Zobel J. Document expansion versus query expansion for ad-hoc retrieval. In: Proceedings of the 10th Australasian Document Computing Symposium. 2005, 34–41Google Scholar

  • 33.

    Shokouhi M, Azzopardi L, Thomas P. Effective query expansion for federated search. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2009, 427–434Google Scholar

  • 34.

    Wang H, Liang Y, Fu L, Xue G R, Yu Y. Efficient query expansion for advertisement search. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2009, 51–58Google Scholar

  • 35.

    Voorhees E M. Query expansion using lexical-semantic relations. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1994, 61–69Google Scholar

  • 36.

    Collins-Thompson K, Callan J. Query expansion using random walk models. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. 2005, 704–711Google Scholar

  • 37.

    Liu S, Liu F, Yu C, Meng W Y. An effective approach to document retrieval via utilizing wordnet and recognizing phrases. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2004, 266–272Google Scholar

  • 38.

    Song M, Song I Y, Hu X H, Allen R B. Integration of association rules and ontologies for semantic query expansion. Data & Knowledge Engineering, 2007, 63(1): 63–75Google Scholar

  • 39.

    Gauch S, Wang J Y, Rachakonda S M. A corpus analysis approach for automatic query expansion and its extension to multiple databases. ACM Transactions on Information Systems, 1999, 17(3): 250–269Google Scholar

  • 40.

    Hu J N, Deng W H, Guo J. Improving retrieval performance by global analysis. In: Proceedings of the 18th International Conference on Pattern Recognition. 2006, 703–706Google Scholar

  • 41.

    Park L A, Ramamohanarao K. Query expansion using a collection dependent probabilistic latent semantic thesaurus. In: Proceedings of the 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 2007, 224–235Google Scholar

  • 42.

    Milne D N, Witten I H, Nichols D M. A knowledge-based search engine powered by wikipedia. In: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management. 2007, 445–454Google Scholar

  • 43.

    Rocchio J J. Relevance feedback in information retrieval. The SMART Retrieval System-Experiments in Automatic Document Processing, 1971, 313–323Google Scholar

  • 44.

    Robertson S E, Jones K S. Relevance weighting of search terms. Journal of the American Society for Information Science, 1976, 27(3): 129–146Google Scholar

  • 45.

    Wong W, Luk R W P, Leong H V, Ho K, Lee D L. Re-examining the effects of adding relevance information in a relevance feedback environment. Information Processing & Management, 2008, 44(3): 1086–1116Google Scholar

  • 46.

    Zhai C X, Lafferty J. Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the 10th International Conference on Information and Knowledge Management. 2001, 403–410Google Scholar

  • 47.

    Lavrenko V, Croft W B. Relevance based language models. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2001, 120–127Google Scholar

  • 48.

    Khennak I, Drias H. Strength pareto fitness assignment for generating expansion features. In: Proceedings of the 3rd World Conference on Information Systems and Technologies. 2015, 133–142Google Scholar

  • 49.

    Robertson S, Zaragoza H. The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends® in Information Retrieval, 2009, 3(4): 333–389Google Scholar

  • 50.

    Robertson S E. On term selection for query expansion. Journal of Documentation, 1990, 46(4): 359–364Google Scholar

  • 51.

    Carpineto C, De Mori R, Romano G, Bigi B. An information-theoretic approach to automatic query expansion. ACM Transactions on Information Systems, 2001, 19(1): 1–27Google Scholar

  • 52.

    Jurafsky D, Martin J H. Speech and Language Processing. Upper Saddle River, NJ: Pearson Prentice Hall, 2014Google Scholar

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