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The feature extraction is the most key technology of text categorization. The word is used as the feature in the traditional text classification, and its effect for the text classification is evidence. The feature extraction method using base phrase and keyword changes the feature extraction of Chinese text from
A text/web document is a knowledge representation of a human idea (a structured set of thoughts). This paper refines TFIDF and extended TFIDF(ETFIDF)[16]; These values really measures the co-occurrences of tokens. The ETFID captures the semantic more accurately. Tokens with high TFIDF values are called keywords. The
The content of a text is mainly defined by keywords and named entities occurring in it. In particular for news articles, named entities are usually important to define their semantics. However, named entities have ontological features, namely, their aliases, types, and identifiers, which are hidden from their textual
proposed a formalized model of the text semantic similarity and similarity algorithm based on the case grammar. The semantic meanings of a sentence stem decide the similarity of a sentence. To the similarity sentence, a vector is used for the decorating case to get similarity algorithm. In this way, it avoided the keyword
scenes by checking the discovered cross-media correlation. To make these two modalities comparable, photos related to the visited scenic spots are retrieved from image search engines, by the keywords extracted from text-based schedules. Sequences of key frames and retrieved photos are represented as visual word histograms
performance. Apart from estimating the best path to follow, our system also expands its initial keywords by using genetic algorithm during the crawling process. To crawl Vietnamese web pages, we apply a hybrid word segmentation approach which consists of combining automata and part of speech tagging techniques for the Vietnamese
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