Learning Category Distribution for Text Classification

Correcting Category Errors in Text Classification

Transfer learning for text classification

Linear text classification algorithms work by computing an inner product between a test document vector and a parameter vector. In many such algorithms, including naive Bayes and most TFIDF variants, the parameters are determined by some simple, closed-form, function of training set statistics; we call this mapping mapping from statistics to parameters, the parameter function. Much research in ...

Active Learning with Rationales for Text Classification

We present a simple and yet effective approach that can incorporate rationales elicited from annotators into the training of any offthe-shelf classifier. We show that our simple approach is effective for multinomial naı̈ve Bayes, logistic regression, and support vector machines. We additionally present an active learning method tailored specifically for the learning with rationales framework.

Pool-Based Active Learning for Text Classification

This paper shows how a text classifier’s need for labeled training documents can be reduced by employing a large pool of unlabeled documents. We modify the Query-by-Committee (QBC) method of active learning to use the unlabeled pool by explicitly estimating document density when selecting examples for labeling. Then active learning is combined with Expectation-Maximization in order to “fill in”...

Learning Non-Linear Functions for Text Classification

In this paper, we show that generative classifiers are capable of learning non-linear decision boundaries and that non-linear generative models can outperform a number of linear classifiers on some text categorization tasks. We first prove that 3-layer multinomial hierarchical generative (Bayesian) classifiers, under a particular independence assumption, can only learn the same linear decision ...