Sentence examples for approaches for text from inspiring English sources

Similar to the work of Frank et al. [27], Teahan and Harper [47] performed extensive experiments to evaluate the performance of different approaches for text categorization on the standard Reuters-21578 collection.

A large-scale comparison of discriminative and generative classification approaches for text-based retrieval of general audio on the Internet was presented in [9].

In this paper, we propose an approach for text categorization based on Dissimilarity Representation and multiple classifier systems.

While there has been a long history of rule-based text classifiers, to the best of our knowledge no M-of-N-based approach for text categorization has so far been proposed.

Blogburst (our coverage) appears to be taking the à la carte approach for text content.

Deep neural networks provide an alternative approach for text mining tasks and feature extraction.

The block scheme of the proposed text classification approach is depicted in Figure 1. Figure 1 The block scheme of the proposed approach for text classification.

The proposed method improves the state-of-the-art color reduction approach for text detection by Nikolaou and Papamarkos [14] with additional SWT information [8].

Recently, Zhang and LeCun [1] proposed a novel approach for text learning tasks where they were able to use CNNs to train classifiers by representing text in an image like, character-level fashion.

The same approach for text classification and statistical language modeling can be also used for several other domains, in the task of broadcast news transcription, meeting speech recognition, etc.

Our proposed approach for text classification based on the key phrase identification, term weighting, measuring similarity between two documents, and automatic thresholding is introduced in the Section 3. Section 4 presents the speech recognition setup used for evaluating language models trained on classified text corpora.