Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of estimating the rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis / opinion mining 1This is a revised and substantially extended version of a paper appeared as (Baccianella et al., 2010). The order in which the a...

Document classiication is one of the central issues in information retrieval research. The aim is to uncover similarities between text documents. In other words, classiication techniques are used to gain insight in the structure of the various data items contained in the text archive. In this paper we show the results from using a hierarchy of self-organizing maps to perform the text classiicat...

Text classification remains an important practical application of both modern machine learning (ML) and natural language processing (NLP) techniques. The influence of these disparate areas of research has contributed much to the success of current state of the art classification methods. This essay provides an overview of the field of text classification, and investigates in particular the topi...

feature extraction for text classification Göksel BİRİCİK∗, Banu DİRİ, Ahmet Coşkun SÖNMEZ Department of Computer Engineering, Yıldız Technical University, Esenler, İstanbul-TURKEY e-mails: {goksel,banu,acsonmez}@ce.yildiz.edu.tr Received: 03.02.2011 Abstract Feature selection and extraction are frequently used solutions to overcome the curse of dimensionality in text classification problems. W...

With the exponential growth of the number of documents available on the Internet, automatic feature selection approaches are increasingly important for the preprocessing of textual documents for data mining. Feature selection, which focuses on identifying relevant data, can help reduce the workload of processing huge amounts of data as well as increase the accuracy for the subsequent data minin...

Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of estimating the rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically rating large amounts of product review data in digital form. As in other super...

In this paper, we focus on feature coverage policies used for feature selection in the text classification domain. Two alternative policies are discussed and compared: corpus-based and class-based selection of features. We make a detailed analysis of pruning and keyword selection by varying the parameters of the policies and obtain the optimal usage patterns. In addition, by combining the optim...

Feature selection has improved the performance of text clustering. Global feature selection tries to identify a single subset of features which are relevant to all clusters. However, the clustering process might be improved by considering different subsets of features for locally describing each cluster. In this work, we introduce the method ZOOM-IN to perform local feature selection for partit...

The Generative Adversarial Network (GAN) has achieved great success in generating realistic (realvalued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long shortterm memory network as generator, and a convolutional network ...

Visual “pop-out” occurs when a unique visual target (e.g. a feature singleton) is present among a set of homogeneous distractors. However, the role of visual awareness in this process remains unclear. Here we show that, even though subjects were not aware of a suppressed pop-out display, their subsequent performance on an orientation discrimination task was significantly better at the pop-out l...