Christos Klavdianos
ABSTRACT
Nowadays1, that the use. of search engines has been expanded due to user requirements, there is a great need to diversify their results in order to cover as many informational needs as possible and not to repeat similar content for a given query. In this context, this paper was initiated, which, using textual commenting techniques on static and dynamic ranking algorithms, applies cutting and merging of unnecessary information, aiming at differentiating the results without decreasing their relevance.
- Agrawal, R., Gollapudi, S., Halverson, A., & Ieong, S. (2009). Diversifying Search Results.WSDM 2009: 5--14. Google Scholar
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- Angel, A., & Koudas, N. (2011). Efficient Diversity-Aware Search. In Proceedings of the SIGMOD Conference, pp. 781--792,2011. Google ScholarDigital Library
- Brandt, C., Joachims, T., Yue, Y., & Bank, J. (2011). Dynamic Ranked Retrieval. WSDM 2011: 247--256. Google ScholarDigital Library
- Clarke, C., Craswell, N. and Soboroff, I. (2009). Overview of the TREC 2009 web track. In 18th Text Retrieval Conference, Gaithersburg, Maryland, 2009.Google Scholar
- Clarke, C., Kolla, M., Cormack, G., Vechtomova, O., Ashkan, A., Büttcher, S., et al. (2008). Novelty and diversity in information retrieval evaluation. In: 31st Annual International ACM SIGIR Conf., Singapore, July 20-24 (2008). Google ScholarDigital Library
- Ferragina, P., & Scaiella, U. (2010). TAGME: On-the-fly Annotation of Short Text Fragments (by Wikipedia Entities).CoRR abs/1006.3498.Google Scholar
- Gollapudi, S., & Sharma, A. (2009). An Axiomatic Approach for Result Diversification.WWW 2009: 381--390. Google ScholarDigital Library
- Kharazmi, S., Sanderson, M., Scholer, F., & Vallet, D. (2014). Using Score Differences for Search Result Diversification. SIGIR 2014: 1143--1146. Google ScholarDigital Library
- Lesk, M. (1986). Automatic sense disambiguation using machine readable dictionaries. SIGDOC 1986: 24--26. Google ScholarDigital Library
- Makris, C., Plegas, Y., Stamatiou, Y., Stavropoulos, E., & Tsakalidis, A. (2014). Reducing Redundant Information in Search Results Employing Approximation Algorithms. DEXA (2) 2014: 240--247. Patras, Greece.Google Scholar
- Mendes, P., Jakob, M., Garcia-Silva, A., & Bizer, C. (2011). DBpedia spotlight: shedding light on the web of documents. I-SEMANTICS 2011: 1--8. Google ScholarDigital Library
- Mihalcea, R., & Csomai, A. (2007). Wikify! Linking Documents to Encyclopedic Knowledge.CIKM 2007: 233--242 Google ScholarDigital Library
- Milne, D., & Witten, I. (2008). Learning to Link with Wikipedia. CIKM 2008: 509--518. Google ScholarDigital Library
- Minack, E., Demartini, G., & Nejdl, W. (2009). Current Approaches to Search Result Diversication.Google Scholar
- Moro, A., Raganato, A., & Navigli, R. (2014). Entity Linking meets Word Sense Disambiguation: a Unified Approach.. TACL 2: 231--244.Google ScholarCross Ref
- Navigli, R. (2003). Word Sense Disambiguation. ACM Computing 41(2), 10:1--10:69. Google ScholarDigital Library
- Plegas, Y., & Stamou, S. (2013). Reducing Information Redundancy in Search Results.SAC 2013: 886--893. Patras, Greece. Google ScholarDigital Library
- Pugh, W., & Henzinger, M. (n.d.). Detecting Duplicate and Near-Duplicate Files. US Patent # 6658423.Google Scholar
- Radlinski, F., Bennett, P., & Yilmaz, E. (2011). Detecting Duplicate Web Documents using Clickthrough In: 4th Intern. Conf. on WSDM, pp. 147--156. Google ScholarDigital Library
- The ClueWeb09 Dataset. Retrieved from http://lemurproject.org/clueweb09.php/Google Scholar
- Vee, E., Srivastava, U., Shanmugasundaram, J., Bhat, P., & Yahia, S. (2009). Efficient Computation of Diverse Query Results.IEEE Data Eng. Bull. 32(4): 57--64.Google Scholar
- Yang, G., Sloan, M., & Wang, Y. (2016). Dynamic Infomation Retrieval Modeling. Synthesis Lectures on Information Concepts, Retrieval, and Services, Morgan & Claypool Publishers. Google ScholarDigital Library
- Yue, Y. (2016), New Learning Frameworks for Information Retrieval Ph.D. Dissertation, Cornell University, January, 2011.Conference (2) 2016: 177--185 Google ScholarDigital Library
- Zhai, C., Cohen, W., & Lafferty, J. (2003). Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. SIGIR 2003: 10--17. Google ScholarDigital Library
- Zhang, Y., Callan, J., & Minka, T. (2002). Novelty and Redundancy Detection in Adaptive Filtering. In Proceedings of the 25th International ACM SIGIR Conference, 2002. Google ScholarDigital Library
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