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.
Sociedad Argentina de Informática e Investigación Operativa
free text keywords: Ciencias Informáticas, temporal networks, word embeddings, semisupervised learning, Natural Language Processing, Semantics