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    » Finding nuggets in documents: A machine learning approach
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    LREC
    2010
    157views Education» more  LREC 2010»
    Is Sentiment a Property of Synsets? Evaluating Resources for Sentiment Classification using Machine Learning
    13 years 11 months ago
    Is Sentiment a Property of Synsets? Evaluating Resources for Sentiment Classification using Machine Learning
    Download  www.lrec-conf.org
    Existing approaches to classifying documents by sentiment include machine learning with features created from n-grams and part of speech. This paper explores a different approach ...
    Aleksander Wawer
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    BMCBI
    2005
    155views more  BMCBI 2005»
    Mining protein function from text using term-based support vector machines
    13 years 10 months ago
    Mining protein function from text using term-based support vector machines
    Download  www.biomedcentral.com
    Background: Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We...
    Simon B. Rice, Goran Nenadic, Benjamin J. Stapley
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    SIGSOFT
    2008
    ACM
    175views Software Engineering» more  SIGSOFT 2008»
    Finding programming errors earlier by evaluating runtime monitors ahead-of-time
    14 years 11 months ago
    Finding programming errors earlier by evaluating runtime monitors ahead-of-time
    Download  www.bodden.de
    Runtime monitoring allows programmers to validate, for instance, the proper use of application interfaces. Given a property specification, a runtime monitor tracks appropriate run...
    Eric Bodden, Patrick Lam, Laurie J. Hendren
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    ICML
    2009
    IEEE
    246views Machine Learning» more  ICML 2009»
    BoltzRank: learning to maximize expected ranking gain
    14 years 11 months ago
    BoltzRank: learning to maximize expected ranking gain
    Download  www.cs.toronto.edu
    Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to o...
    Maksims Volkovs, Richard S. Zemel
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    ICML
    2005
    IEEE
    118views Machine Learning» more  ICML 2005»
    Why skewing works: learning difficult Boolean functions with greedy tree learners
    14 years 11 months ago
    Why skewing works: learning difficult Boolean functions with greedy tree learners
    Download  www.machinelearning.org
    We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the...
    Bernard Rosell, Lisa Hellerstein, Soumya Ray, Davi...
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