There are many situations in which problems deal with vague and imprecise information. In such cases, the information could be modelled by means of numbers, however, it doesn’t seem logical to model imprecise information in a precise way. Therefore, the use of linguistic modelling have been used with successful results in these problems. The use of linguistic information involves the need of ca...

Languages tend not to exhibit unpredictable variation, and learners receiving variable linguistic input tend to eliminate it, making the language more regular. We explore how this behavior is influenced by social cues, in particular when variability is distributed within and across teachers. We trained participants on an artificial language that contained lexical variability and manipulated how...

Document-level sentiment analysis can benefit from fine-grained subjectivity, so that sentiment polarity judgments are based on the relevant parts of the document. While finegrained subjectivity annotations are rarely available, encouraging results have been obtained by modeling subjectivity as a latent variable. However, latent variable models fail to capitalize on our linguistic knowledge abo...

In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule-based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results...

The paper proposed a linguistic PROMETHEE approach for making investment portfolio decision. First, we both consider quantitative and qualitative information of each stock and use 2-tuple linguistic value for experts to express their opinions to obtain qualitative information with respect to all evaluation criteria. Then, the transformation method of 2-tuple linguistic variable is applied to ag...

The efficiency of pattern recognition algorithms is highly conditioned to a proper definition of the patterns assumed to structure the data. The multigram model provides a statistical tool to retrieve sequential variable-length regularities within streams of data. In this paper, we present a general formulation of the model, applicable to single or multiple parallel strings of data having eithe...

Hypothesis testing of population parameters plays an important role in statistical analysis. This work extends this topic to the fuzzy environment for wider application. Developing a method of testing fuzzy hypotheses using linguistic variables is of priority concern. The method is based on Zadeh’s extension principle: the fuzzy population mean in the null hypothesis is converted to fuzzy numbe...