Volume 15, Issue 4 (3-2019)                   JSDP 2019, 15(4): 71-84 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

paksima J. A novel model for phrase searching based-on Minimum Weighted Relocation Model. JSDP. 2019; 15 (4) :71-84
URL: http://jsdp.rcisp.ac.ir/article-1-670-en.html
Payam Noor university
Abstract:   (1346 Views)

Finding high-quality web pages is one of the most important tasks of search engines. The relevance between the documents found and the query searched depends on the user observation and increases the complexity of ranking algorithms. The other issue is that users often explore just the first 10 to 20 results while millions of pages related to a query may exist. So search engines have to use suitable algorithms with high performance to find the most relevant pages.
The ranking section is an important part of search engines. Ranking is a process in which the web page quality is estimated by the search engine. There are two main methods for ranking web pages. In the first method, ranking is done based on the documents’ content (traditional rankings). Models, such as Boolean model, probability model and vector space model are used to rank documents based on their contents. In the second method, based on the graph, web connections and the importance of web pages, ranking process is performed.
Based on researches on search engines, the majority of user queries is more than one term. For queries with more than one term, two models can be used. The first model assumes that query terms are independent of each other while the second model considers a location and order dependency between query terms. Experiments show that in the majority of queries there are dependencies between terms. One of the parameters that can specify dependencies between query terms is the distance between query terms in the document. In this paper, a new definition of distance based on Minimum Weighted Displacement Model (MWDM) of document terms to accommodate the query terms is presented. In the Minimum Weighted Displacement Model (MWDM), we call the minimum number of words moving a text to match the query term by space.
In addition, because most of the ranking algorithms use the TF (Term Frequency) to score documents and for queries more than one term, there is no clear definition of these parameters; in this paper, according to the new distance concept, Phrase Frequency and Inverted Document Frequency are defined. Also, algorithms to calculate them are presented. The results of the proposed algorithm compared with multiple corresponding algorithms shows a favorable increase in average precision.
 

Full-Text [PDF 13146 kb]   (253 Downloads)    
Type of Study: بنیادی | Subject: Paper
Received: 2017/11/16 | Accepted: 2019/01/9 | Published: 2019/03/8 | ePublished: 2019/03/8

References
1. [1] A. Z. Bidoki, "Effective Web Ranking and Crawling(in persian)," University of Tehran, 2009.
2. [2] R. Baeza-Yates and B. Ribeiro-Neto, "Modern information retrieval," New York, vol. 9, p. 513, 1999.
3. [3] G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Informa-tion Processing and Management, vol. 24, no. 5, pp. 513-523, 1988. [DOI:10.1016/0306-4573(88)90021-0]
4. [4] S. E. Robertson, Overview of the Okapi projects, vol. 53, no. 1. MCB UP Ltd, 1997, pp. 3-7. [DOI:10.1108/EUM0000000007186]
5. [5] Y. Zhang and A. Moffat, "Some Observations on User Search Behaviour.," Austr. J. Intelligent Information Processing Systems, vol. 9, no. 2, pp. 1-8, 2006.
6. [6] D. Bahle, H. Williams, and J. Zobel, "Compaction techniques for nextword indexes," in String Processing and Information Retrieval, Interna-tional Symposium on, 2001, p. 33.
7. [7] H. E. Williams, J. Zobel, and D. Bahle, "Fast phrase querying with combined indexes," ACM Transactions on Information Systems (TOIS), vol. 22, no. 4, pp. 573-594, 2004. [DOI:10.1145/1028099.1028102]
8. [8] A. Doucet and H. Ahonen-Myka, "An efficient any language approach for the integration of phrases in document retrieval," Language resources and evaluation, vol. 44, no. 1-2, pp. 159-180, 2010. [DOI:10.1007/s10579-009-9102-3]
9. [9] I. H. Witten, A. Moffat, and T. C. Bell, Managing gigabytes: compressing and indexing documents and images. Morgan Kaufmann, 1999.
10. [10] D. Bahle, "Efficient Phrase Querying," School of Computer Science and Information Technology, Royal Melbourne Institute of Technology, 2003.
11. [11] A. Fellinghaug, "Phrase searching in text index-es," no. June, p. 137, 2008.
12. [12] C. J. van Rijsbergen, "A theoretical basis for the use of co-occurrence data in information retrie-val," Journal of documentation, vol. 33, no. 2, pp. 106-119, 1977. [DOI:10.1108/eb026637]
13. [13] R. Nallapati and J. Allan, "Capturing term dependencies using a language model based on sentence trees," in Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp. 383-390. [DOI:10.1145/584853.584855]
14. [14] E. M. Keen, "The use of term position devices in ranked output experiments," Journal of Documentation, vol. 47, no. 1, pp. 1-22, 1991. [DOI:10.1108/eb026869]
15. [15] W. B. Croft, H. R. Turtle, and D. D. Lewis, "The use of phrases and structured queries in information retrieval," in Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval, 1991, pp. 32-45. [DOI:10.1145/122860.122864]
16. [16] D. Metzler and W. B. Croft, "A Markov random field model for term dependencies," in Proceed-ings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 2005, pp. 472-479. [DOI:10.1145/1076034.1076115]
17. [17] E. K. F. Dang, R. W. P. Luk, and J. Allan, "A context-dependent relevance model," Journal of the Association for Information Science and Technology, 2015. [DOI:10.1002/asi.23419]
18. [18] F. Song and W. B. Croft, "A general language model for information retrieval," in Proceedings of the eighth international conference on In-formation and knowledge management, 1999, pp. 316-321. [DOI:10.1145/319950.320022] [PMCID]
19. [19] J. Gao, J.-Y. Nie, G. Wu, and G. Cao, "Dependence language model for information retrieval," in Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrie-val, 2004, pp. 170-177. [DOI:10.1145/1008992.1009024]
20. [20] B. He, J. X. Huang, and X. Zhou, "Modeling term proximity for probabilistic information retrieval models," Information Sciences, vol. 181, no. 14, pp. 3017-3031, 2011. [DOI:10.1016/j.ins.2011.03.007]
21. [21] Y. Rasolofo and J. Savoy, Term proximity scoring for keyword-based retrieval systems. Springer, 2003. [DOI:10.1007/3-540-36618-0_15]
22. [22] C. Eickhoff, A. P. de Vries, and T. Hofmann, "Modelling Term Dependence with Copulas," in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 783-786. [DOI:10.1145/2766462.2767831]
23. [23] S. Büttcher, C. L. A. Clarke, and B. Lushman, "Term proximity scoring for ad-hoc retrieval on very large text collections," in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp. 621-622. [DOI:10.1145/1148170.1148285]
24. [24] T. Tao and C. Zhai, "An exploration of proximity measures in information retrieval," in Proceed-ings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 295-302. [DOI:10.1145/1277741.1277794]
25. [25] J. Zhao and Y. Yun, "A proximity language model for information retrieval," in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009, pp. 291-298. [DOI:10.1145/1571941.1571993]
26. [26] J. Zhao, J. X. Huang, and B. He, "CRTER: using cross terms to enhance probabilistic information retrieval," in Proceedings of the 34th inter-national ACM SIGIR conference on Research and development in Information Retrieval, 2011, pp. 155-164. [DOI:10.1145/2009916.2009941] [PMCID]
27. [27] J. Zhao, J. X. Huang, and Z. Ye, "Modeling term associations for probabilistic information retrieval," ACM Transactions on Information Systems (TOIS), vol. 32, no. 2, p. 7, 2014. [DOI:10.1145/2590988]
28. [28] J. Miao, J. X. Huang, and Z. Ye, "Proximity-based rocchio's model for pseudo relevance," in Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 2012, pp. 535-544. [DOI:10.1145/2348283.2348356]
29. [29] C. L. A. Clarke, G. V. Cormack, and E. A. Tudhope, "Relevance ranking for one to three term queries," Information Processing & Management, vol. 36, no. 2, pp. 291-311, 2000. [DOI:10.1016/S0306-4573(99)00017-5]
30. [30] J. Klekota, F. P. Roth, and S. L. Schreiber, "Query Chem: a Google-powered web search combining text and chemical structures," Bioin-formatics, vol. 22, no. 13, pp. 1670-1673, 2006. [DOI:10.1093/bioinformatics/btl155] [PMID]
31. [31] K. Sadakane and H. Imai, "Text Retrieval by using k-word Proximity Search," in Database Applications in Non-Traditional Environments, 1999.(DANTE'99) Proceedings. 1999 Inter-national Symposium on, 1999, pp. 183-188.
32. [32] X. Lu, A. Moffat, and J. S. Culpepper, "On the cost of extracting proximity features for term-dependency models," in CIKM 2015, 2015, pp. 293-302. [DOI:10.1145/2806416.2806467]
33. [33] M. Blum, R. W. Floyd, V. Pratt, R. L. Rivest, and R. E. Tarjan, "Time bounds for selection," Journal of computer and system sciences, vol. 7, no. 4, pp. 448-461, 1973. [DOI:10.1016/S0022-0000(73)80033-9]
34. [34] R. Courant, Differential and integral calculus, vol. 2. John Wiley & Sons, 2011.
35. [35] S. E. Robertson and S. Walker, "Some for Simple Effective Approximations to the 2 - Poisson Model Probabilistic Weighted Retrieval," Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 232-241, 1994. [DOI:10.1007/978-1-4471-2099-5_24]
36. [36] H. Zaragoza, N. Craswell, M. J. Taylor, S. Saria, and S. E. Robertson, "Microsoft Cambridge at TREC 13: Web and Hard Tracks.," in TREC, 2004, vol. 4, p. 1.
37. [37] R. Duda O., P. Hart E., and D. Stork G., Pattern Classification. 2000.
38. [38] S. Robertson and H. Zaragoza, The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc, 2009. [DOI:10.1561/1500000019]
39. [39] J. Zhao and J. X. Huang, "An enhanced context-sensitive proximity model for probabilistic information retrieval," in Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014, pp. 1131-1134. [DOI:10.1145/2600428.2609527] [PMCID]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


© 2015 All Rights Reserved | Signal and Data Processing