Sentence examples for classification of texts from inspiring English sources

Kowata (2009) followed Bain's (1890) classification of texts, which consists of narrations, descriptions, expositions, and argumentations to analyse the writing tasks in the Japanese university entrance examinations.

However, since such judgements are made by analysts already today (although manually), creation of algorithms that classify posts according to the same criteria would be no different from todays' situation (except for that the classification of texts then can be made on a much larger scale).

These three teachings form the basis for the later classification of texts in the Daozang.

Based on it, our system performs the following three fundamental steps to detect the table zones: classification of the text and non-text elements in the document image, detection of the ruling-line tables, and identification of the non-ruling-line tables.

According to experimental results, compared with traditional feature extraction methods, this method is more suitable for the classification of short texts.

The database has been employed for mode (content type) detection [53], keyword spotting [54] and classification of text/non-text objections [55].

For classification of these text segments to the in-domain and out-of domain area, document similarity with automatic thresholding are used.

The first step in the process of classification of the text data is to propose an algorithm for extracting key phrases from examined domain (from development data).

Hence, our research shows that albeit low, the inclusion of verb terms in addition to noun terms helps to improve the classification of short text.

The threshold value was calculated on a development data set and its acquisition shares the same process with classification of the text data described in the previous sections.

The relevant keywords are extracted and used for the classification of collated text (Bartov et al. 2015; Da et al. 2015; Sul et al. 2016).