Our algorithm is based on identifying key phrases with their occurrences in short text segments.
The proposed algorithm for text classification is based on detection of theme in short text segments based on the most frequent key phrases.
This simple and effective algorithm classifies short text segments (such as paragraphs) from heterogeneous text corpora gathered from various resources to the in-domain and out-of-domain data.
This means that the development data were divided into short text segments consisting of at least 300 words, represented by VSM through the key phrases, and weighted, and each document was compared with the reference text (weighted list of key phrases) using one of the presented similarity measure.
By application of segmentation rules, we obtained a total of 6,908,655 short (300+ words) text segments - documents - entering to the process of text classification.
The text segment realizing the Headline provides a short restatement of the text segment serving as the Lead, whereas the text segment serving as the Lead Development provides a much more detailed elaboration and another more local elaboration.
Frivolous communication, including dating, happens increasingly through short text messages, which are cheap.Although China's overall market is a long way from saturation, the lucrative segment of it may not be far off.
The short text below corrects a mistake in the proof of [1, Lemma 4.1].
Library Communications also provides a short text.
While reading, students rated novelty, curiosity, impressiveness, importance, and willingness to reflect on each text segment.
