Augmenting Naive Bayes Classifiers with Statistical Language Models

We augment naive Bayes models with statistical n-gram language models to address short- comings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier which allows for a local Markov dependence among observations; a model we re- fer to as the Chain Augmented Naive Bayes (CAN) Bayes classifier. CAN models have two advantages over standard naive Bayes classifiers. First, they relax some of the indepen- dence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed variables—while still permitting efficient inference and learning. Second, they permit straight- forward application of sophisticated smoothing techniques from statistical language modeling, which allows one to obtain better parameter estimates than the standard Laplace smoothing used in naive Bayes classification. In this paper, we introduce CAN models and apply them to various text classification problems. To demonstrate the language independent and task independent nature of these classifiers, we present experimental results on several text clas- sification problems—authorship attribution, text genre classification, and topic detection—in several languages—Greek, English, Japanese and Chinese. We then systematically study the key factors in the CAN model that can influence the classification performance, and analyze the strengths and weaknesses of the model

Independence of Odor Quality and Absolute Sensitivity in a Study of Aging

Young, middle-aged, and senior subjects performed tasks designed to examine whether odor quality discrimination varies independently of sensitivity. One task entailed detection of 2-heptanone and the others AB-X discrimination of quality for sets of 2-heptanone and homologues or 2-heptanone and non-ketones. Subjects sought to discriminate either at intensity-matched concentrations far above threshold, but fixed across subjects, or at levels adjusted to neutralize differences in sensitivity. The young and middle-aged groups manifested the same absolute sensitivity, but the senior group poorer sensitivity. Performance in quality discrimination, however, declined progressively. Performance lacked an association with absolute sensitivity, no matter how examined. These data, in conjunction with converging findings from patients with neurological damage, studies of brain imaging, and the relation between concentration and quality discrimination in younger persons, suggest largely independent processing of odor quality and intensity

Faculty Research in Progress, 2018-2019

The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty\u27s research continues to find its way into the very best industrial relations, social science and statistics journal