-
Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
-
Saz, J.T.: Perspectivas en recuperacion y explotacion de informacion electronica : el 'data mining' (1997)
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Perugini, S.; Ramakrishnan, N.: Mining Web functional dependencies for flexible information access (2007)
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- Abstract
- We present an approach to enhancing information access through Web structure mining in contrast to traditional approaches involving usage mining. Specifically, we mine the hardwired hierarchical hyperlink structure of Web sites to identify patterns of term-term co-occurrences we call Web functional dependencies (FDs). Intuitively, a Web FD x -> y declares that all paths through a site involving a hyperlink labeled x also contain a hyperlink labeled y. The complete set of FDs satisfied by a site help characterize (flexible and expressive) interaction paradigms supported by a site, where a paradigm is the set of explorable sequences therein. We describe algorithms for mining FDs and results from mining several hierarchical Web sites and present several interface designs that can exploit such FDs to provide compelling user experiences.
-
Chen, Y.-L.; Liu, Y.-H.; Ho, W.-L.: ¬A text mining approach to assist the general public in the retrieval of legal documents (2013)
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Song, J.; Huang, Y.; Qi, X.; Li, Y.; Li, F.; Fu, K.; Huang, T.: Discovering hierarchical topic evolution in time-stamped documents (2016)
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-
Chowdhury, G.G.: Template mining for information extraction from digital documents (1999)
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- Date
- 2. 4.2000 18:01:22
-
Wei, C.-P.; Lee, Y.-H.; Chiang, Y.-S.; Chen, C.-T.; Yang, C.C.C.: Exploiting temporal characteristics of features for effectively discovering event episodes from news corpora (2014)
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-
KDD : techniques and applications (1998)
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- Footnote
- A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
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Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004)
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-
Gaizauskas, R.; Wilks, Y.: Information extraction : beyond document retrieval (1998)
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Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010)
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Qiu, X.Y.; Srinivasan, P.; Hu, Y.: Supervised learning models to predict firm performance with annual reports : an empirical study (2014)
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Loonus, Y.: Einsatzbereiche der KI und ihre Relevanz für Information Professionals (2017)
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Liu, Y.; Huang, X.; An, A.: Personalized recommendation with adaptive mixture of markov models (2007)
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Liu, Y.; Zhang, M.; Cen, R.; Ru, L.; Ma, S.: Data cleansing for Web information retrieval using query independent features (2007)
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-
Li, D.; Tang, J.; Ding, Y.; Shuai, X.; Chambers, T.; Sun, G.; Luo, Z.; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging (2015)
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Tu, Y.-N.; Hsu, S.-L.: Constructing conceptual trajectory maps to trace the development of research fields (2016)
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-
Varathan, K.D.; Giachanou, A.; Crestani, F.: Comparative opinion mining : a review (2017)
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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.
-
Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997)
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- Date
- 22.11.1998 18:57:22
-
Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999)
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- Date
- 17. 7.2002 19:22:06
