A non-syntactic approach for text sentiment classification with stopwords

The present approach uses stopwords and the gaps that oc- cur between successive stopwords –formed by contentwords– as features for sentiment classification.

A regularized linear classifier for effective text classification

In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

A Perspective on Computer Assisted Assessment Techniques for Short Free-Text Answers

Computer Assisted Assessment (CAA) has been existing for several years now. While some forms of CAA do not require sophisticated text understanding (e.g., multiple choice questions), there are also student answers that consist of free text and require analysis of text in the answer. Research towards the latter till date has concentrated on two main sub-tasks: (i) grading of essays, which is done mainly by checking the style, correctness of grammar, and coherence of the essay and (ii) assessment of short free-text answers. In this paper, we present a structured view of relevant research in automated assessment techniques for short free-text answers. We review papers spanning the last 15 years of research with emphasis on recent papers. Our main objectives are two folds. First we present the survey in a structured way by segregating information on dataset, problem formulation, techniques, and evaluation measures. Second we present a discussion on some of the potential future directions in this domain which we hope would be helpful for researchers.