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» Text Mining at the Term Level
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135
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KDD
2007
ACM
168views Data Mining» more  KDD 2007»
16 years 4 months ago
Finding tribes: identifying close-knit individuals from employment patterns
We present a family of algorithms to uncover tribes--groups of individuals who share unusual sequences of affiliations. While much work inferring community structure describes lar...
Lisa Friedland, David Jensen
124
Voted
FQAS
2006
Springer
186views Database» more  FQAS 2006»
15 years 7 months ago
Query Phrase Suggestion from Topically Tagged Session Logs
Searchers' difficulty in formulating effective queries for their information needs is well known. Analysis of search session logs shows that users often pose short, vague quer...
Eric C. Jensen, Steven M. Beitzel, Abdur Chowdhury...
158
Voted
BMCBI
2010
154views more  BMCBI 2010»
15 years 3 months ago
LINNAEUS: A species name identification system for biomedical literature
Background: The task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data min...
Martin Gerner, Goran Nenadic, Casey M. Bergman
WWW
2006
ACM
16 years 4 months ago
Background knowledge for ontology construction
In this paper we describe a solution for incorporating background knowledge into the OntoGen system for semi-automatic ontology construction. This makes it easier for different us...
Blaz Fortuna, Marko Grobelnik, Dunja Mladenic
139
Voted
EPIA
2009
Springer
15 years 10 months ago
Classifying Documents According to Locational Relevance
This paper presents an approach for categorizing documents according to their implicit locational relevance. We report a thorough evaluation of several classifiers designed for th...
Ivo Anastácio, Bruno Martins, Pável ...