Large-Scale Bayesian Logistic Regression for Text Categorization

Genkin, Alexander; Lewis, David; Madigan, David

doi:10.1198/004017007000000245

Cited by 639 publications

(522 citation statements)

References 36 publications

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“…Some recent studies Genkin et al 2006) have shown that some variations of Winnow and Bayesian regression are also very promising. Below, we compare the performance of several representative learning methods for authorship attribution.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

461

278

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Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample.In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant.

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Section: Machine Learning Approachmentioning

confidence: 99%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

461

278

View full text Add to dashboard Cite

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“…10.3. The xml of this LU contains basic morpho-syntactic information (lines 3-8), some semantics (lines [11][12][13][14][15] and additional examples on the combinatorial behaviour of the word such as the lexical collocation de band oppompen (to inflate a tire) at line 41, and an idiomatic usage: uit de band springen (excessive behavior) at line 20.…”

Section: Lexical Unitsmentioning

confidence: 99%

“…Next, we used the patterns that were found by the machine learner to identify other word pairs that could be related by hypernymy. We used the same machine learner as [26]: Bayesian Logistic Regression [15].…”

Section: Acquisition Toolkit For Hypernym Pairsmentioning

confidence: 99%

Cornetto: A Combinatorial Lexical Semantic Database for Dutch

Vossen

Maks

Segers

et al. 2012

Essential Speech and Language Technology for Dutch

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General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. IntroductionOne of the goals of the STEVIN programme is the realisation of a digital infrastructure that will enforce the position of the Dutch language in the modern information and communication technology. A semantic database for Dutch is a crucial component for this infrastructure for three reasons: (1) it enables the development of semantic web applications on top of knowledge and information expressed in Dutch, (2) it provides people with access to information systems and services through their native Dutch language and (3) it will connect the Dutch language to the semantic P. Vossen ( ) I. Maks H. van der Vliet

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“…The LASSO achieves accurate prediction by shrinking some coefficients and setting others to zero. A logistic regression version of the LASSO method was described by Genkin et al [13] in which the linear model where ≥ 0 is called the tuning parameter and  is the log-likelihood function:…”

Section: The Least Absolute Shrinkage and Selection Operator (Lasso)mentioning

confidence: 99%

Action following the discovery of a global association between the whole genome and adverse event risk in a clinical drug‐development programme

Whitehead

Kelly

Zhou

et al. 2008

Pharmaceutical Statistics

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Observation of adverse drug reactions during drug development can cause closure of the whole programme. However, if association between the genotype and the risk of an adverse event is discovered, then it might suffice to exclude patients of certain genotypes from future recruitment. Various sequential and non‐sequential procedures are available to identify an association between the whole genome, or at least a portion of it, and the incidence of adverse events. In this paper we start with a suspected association between the genotype and the risk of an adverse event and suppose that the genetic subgroups with elevated risk can be identified. Our focus is determination of whether the patients identified as being at risk should be excluded from further studies of the drug. We propose using a utility function to determine the appropriate action, taking into account the relative costs of suffering an adverse reaction and of failing to alleviate the patient's disease. Two illustrative examples are presented, one comparing patients who suffer from an adverse event with contemporary patients who do not, and the other making use of a reference control group. We also illustrate two classification methods, LASSO and CART, for identifying patients at risk, but we stress that any appropriate classification method could be used in conjunction with the proposed utility function. Our emphasis is on determining the action to take rather than on providing definitive evidence of an association. Copyright © 2008 John Wiley & Sons, Ltd.

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Large-Scale Bayesian Logistic Regression for Text Categorization

Cited by 639 publications

References 36 publications

Computational methods in authorship attribution

Computational methods in authorship attribution

Cornetto: A Combinatorial Lexical Semantic Database for Dutch

Action following the discovery of a global association between the whole genome and adverse event risk in a clinical drug‐development programme

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