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In this paper we present an unsupervised technique for validating the existence of verbal phraseological units in raw text. This technique employs the concept of internal and contextual attraction which basically considers a mathematical formula based on co-occurrence of terms inside and outside of the terms considered to be part of a verbal phraseological unit. The experiments carried out using a corpus of news stories report a 60% of accuracy, which highlights the challenging task of automatic validation of verbal phraseological units in raw texts. [ABSTRACT FROM AUTHOR]

With the rapid growth of user-generated contents online, unsupervised methods which do not need to use labeled training data have become increasingly important in sentiment classification. But the performance of unsupervised methods is unsatisfactory. This is because sentence structure and ambiguity of sentiment intensity are usually ignored in existing unsupervised methods. To address these problems, we propose a multi-granularity fuzzy computing model which involves two innovations. Firstly, we come up with a multi-granularity computing method to compute sentiment intensity of reviews. To be specific, we deconstruct those reviews into three levels of language units-words, phrases and sentences, and consequently manage to compute the sentiment intensity of reviews by combining rule-based methods and statistic-based methods. Secondly, a fuzzy classifier is constructed to solve the ambiguity of sentiment intensity. Furthermore, two different selfsupervised methods using pseudo-labeled training data are proposed to learn the optimum parameters of the fuzzy classifier. Experimental results in four different datasets prove that our model improves 6.25% more accuracy on average than the competitive baselines in sentiment classification of Chinese reviews. [ABSTRACT FROM AUTHOR]