Search (17 results, page 1 of 1)

  • × author_ss:"Crestani, F."
  1. Crestani, F.; Mizzaro, S.; Scagnetto, I,: Mobile information retrieval (2017) 0.05
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    Date
    29. 9.2018 13:24:44
    LCSH
    Computer science
    Text processing (Computer science)
    Series
    Springer briefs in computer science
    Subject
    Computer science
    Text processing (Computer science)
  2. Crestani, F.; Lee, P.L.: Searching the web by constraining spreading activities (2000) 0.03
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    Source
    Information processing and management. 36(2000) no.4, S.585-605
  3. Sweeney, S.; Crestani, F.; Losada, D.E.: 'Show me more' : incremental length summarisation using novelty detection (2008) 0.02
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    Date
    29. 7.2008 19:35:12
    Source
    Information processing and management. 44(2008) no.2, S.663-686
  4. Simeoni, F.; Yakici, M.; Neely, S.; Crestani, F.: Metadata harvesting for content-based distributed information retrieval (2008) 0.02
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    Abstract
    We propose an approach to content-based Distributed Information Retrieval based on the periodic and incremental centralization of full-content indices of widely dispersed and autonomously managed document sources. Inspired by the success of the Open Archive Initiative's (OAI) Protocol for metadata harvesting, the approach occupies middle ground between content crawling and distributed retrieval. As in crawling, some data move toward the retrieval process, but it is statistics about the content rather than content itself; this grants more efficient use of network resources and wider scope of application. As in distributed retrieval, some processing is distributed along with the data, but it is indexing rather than retrieval; this reduces the costs of content provision while promoting the simplicity, effectiveness, and responsiveness of retrieval. Overall, we argue that the approach retains the good properties of centralized retrieval without renouncing to cost-effective, large-scale resource pooling. We discuss the requirements associated with the approach and identify two strategies to deploy it on top of the OAI infrastructure. In particular, we define a minimal extension of the OAI protocol which supports the coordinated harvesting of full-content indices and descriptive metadata for content resources. Finally, we report on the implementation of a proof-of-concept prototype service for multimodel content-based retrieval of distributed file collections.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.1, S.12-24
  5. Varathan, K.D.; Giachanou, A.; Crestani, F.: Comparative opinion mining : a review (2017) 0.02
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    Abstract
    Opinion mining refers to the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyze public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining which deals with identifying and extracting information that is expressed in a comparative form (e.g., "paper X is better than the Y"). Comparative opinion mining plays a very important role when one tries to evaluate something because it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles. One from the perspective of techniques and the other from the perspective of comparative opinion elements. It also incorporates preprocessing tools as well as data set that were used by past researchers that can be useful to future researchers in the field of comparative opinion mining.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.4, S.811-829
  6. Crestani, F.: Combination of similarity measures for effective spoken document retrieval (2003) 0.02
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    Source
    Journal of information science. 29(2003) no.2, S.87-96
  7. Agosti, M.; Crestani, F.; Melucci, M.: Design and implementation of a tool for the automatic construction of hypertexts for information retrieval (1996) 0.01
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    Source
    Information processing and management. 32(1996) no.4, S.459-476
  8. Agosti, M.; Crestani, F.; Melucci, M.: On the use of information retrieval techniques for the automatic construction of hypertext (1997) 0.01
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    Source
    Information processing and management. 33(1997) no.2, S.133-144
  9. Crestani, F.; Vegas, J.; Fuente, P. de la: ¬A graphical user interface for the retrieval of hierarchically structured documents (2004) 0.01
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    Source
    Information processing and management. 40(2004) no.2, S.269-289
  10. Crestani, F.; Wu, S.: Testing the cluster hypothesis in distributed information retrieval (2006) 0.01
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    Source
    Information processing and management. 42(2006) no.5, S.1137-1150
  11. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.01
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    Date
    22. 3.2003 19:27:36
    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.281-284
  12. Crestani, F.; Du, H.: Written versus spoken queries : a qualitative and quantitative comparative analysis (2006) 0.01
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    Date
    5. 6.2006 11:22:23
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.7, S.881-890
  13. Crestani, F.; Rijsbergen, C.J. van: Information retrieval by imaging (1996) 0.00
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    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  14. Tombros, T.; Crestani, F.: Users' perception of relevance of spoken documents (2000) 0.00
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    Source
    Journal of the American Society for Information Science. 51(2000) no.10, S.929-939
  15. Bache, R.; Baillie, M.; Crestani, F.: Measuring the likelihood property of scoring functions in general retrieval models (2009) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.6, S.1294-1297
  16. Keikha, M.; Crestani, F.; Carman, M.J.: Employing document dependency in blog search (2012) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.354-365
  17. Giachanou, A.; Rosso, P.; Crestani, F.: ¬The impact of emotional signals on credibility assessment (2021) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.9, S.1117-1132