Sentence examples for methods for text from inspiring English sources

Growing interest in wearable tech, and the prospect of devices from major players like Google and Apple on the horizon have only served to fuel additional interest in alternative input methods for text and numbers.

However, the analysis of large document corpora requires appropriate automated methods for text mining and analysis that are able to take into account the redundant and predictable nature of formalized discourse.

Discrete codes were developed using traditional methods for text analysis [ 55- 57].

Briefly, we used Support Vector Machines (SVMs), which are state-of-the-science methods for text classification.

This paper is organized as follows: we provide an overview of the existing frequency-based feature selection methods for text classification in Section 2, and this is followed by a definition of the proposed context similarity-based feature selection methods.

Different from traditional methods for text classification e.g. bag-of-words features with support vector machine (SVM) or logistic regression, our method generates new features using the co-occurrence of existing features in big unlabeled data, thus incorporating richer information to overcome data sparseness and leading to more robust performance.

It is claimed typing with the app is up to six times faster than existing methods for texting without sight.

Besides the Naive Bayes method, some scholars have used the maximum entropy method for text classification.

This extension includes a unified energy-based computational framework for visual and audio saliency estimation and a method for text saliency computation (Sec. 4).

ICIP15 extents this baseline method for text saliency computation using also affective modeling, based on the assumption that "semantic similarity can be translated to affective similarity" [92].

The Support Vector Machine (SVM), a supervised learning method, is a prominent method for text classification (Pang et al., 2002; Mullen & Collier, 2004; Whitelaw et al., 2005; Ni et al., 2007).