calcDPC: An Algorithm for Analysing Psycholinguistics Aspects in Machine Translations This work presents calcDPC, an algorithm to analyze psychological and linguistic changes in texts translated from English to Brazilian Portuguese, and compare differences between automatic and humantranslated texts. For this purpose, we use the Brazilian Portuguese version of a tool named LIWC, that distributes lexical words in categories with linguistic and psychological characteristics. By counting words in categories, this work seeks to evaluate the percentage of psycholinguistic changes in automatically translated texts and compare them with a reliable translation performed by a human expert. In this way, this study aims to contribute to the improvement of automatic translation tools. Experimental results indicate promising directions for further research.

LexiconGrammar Tables Development for Arabic Psychological Verbs The identification of psychological verbs is very important in corpora analyses in order to give the polarity of a given text and define the emotional component. The classification of those verbs represents a challenge for linguists since they classify them according to their needs and their understanding. The aim of this paper is the identification and classification of Arabic psychological verbs through lexicongrammar tables that are well structured, easy to use for linguists and allow them to describe all the grammatical, syntactic and semantic characteristics of the lexicon. In this work we create lexicongrammar tables of Arabic psychological verbs with about 400 verbs entries in three main classes and subclasses to use them in lexical, syntactic and semantic analyzers. Using NooJ as an automatic natural language processing platform, we can automatically recognize Arabic psychological verbs by transforming our lexicongrammar tables into NooJ dictionaries and syntactic grammars enabling the detection of those verbs in texts and corpora.