Diachronic Analysis of the Italian Language exploiting Google Ngram

English. In this paper, we propose several methods for the diachronic analysis of the Italian language. We build several models by exploiting Temporal Random Indexing and the Google Ngram dataset for the Italian language. Each proposed method is evaluated on the ability to automatically identify meaning shift over time. To this end, we introduce a new dataset built by looking at the etymological information reported in some dictionaries. Italiano. In questo lavoro proponiamo alcuni metodi per l’analisi diacronica della lingua italiana. Abbiamo costruito differenti modelli utilizzando la tecnica del Temporal Random Indexing e Google Ngram per l’italiano. Ciascun metodo proposto è stato valutato rispetto alla capacità di identificare automaticamente i cambi di significato nel tempo. A tale scopo introduciamo uno nuovo dataset costruito mediante le informazioni etimologiche presenti in alcuni dizionari. 1 Motivation and Background Languages can be studied from two different and complementary viewpoints: the diachronic perspective considers the evolution of a language over time, while the synchronic perspective describes the language rules at a specific point of time without taking its history into account (De Saussure, 1983). In this work, we focus on the diachronic approach, since language appears to be unquestionably immersed in the temporal dimension. Language is subject to a constant evolution driven by the need to reflect the continuous changes of the world. The evolution of word meanings has been studied for several centuries, but this kind of investigation has been limited by the low amount of data on which to perform the analysis. Moreover, in order to reveal structural changes in word meanings, this analysis has to explore long periods