Semi-supervised learning of types of temporal meanings in the Spanish lexicon

Abstract

This paper presents a series of semi-supervised learning algorithms which were designed to classify words or expressions with temporal meanings. The algorithms use a set of pre-tagged temporal expressions and a set of semantic classes which were defined within a research project on the lexical coding of temporal meaning in Spanish. The algorithms in this article are mostly based on word embeddings, but they also make use of other methods. The results obtained strongly depend on the temporal classes considered, but, for some classes, results have reached 90% precision or above.