Parsing temporal and spatial information

In this paper we present a dependency treebank morphologically and syntactically annotated in a specific scheme. We managed to increase the accuracy of the POS-tagger and the syntactic parser used, which led to the increase in the volume of annotated texts. First, we analysed the accuracy with which the syntactic parser recognizes the 14 types of circumstantial complements, especially the temporal and spatial ones. These are the most numerous circumstantial complements, and they are very important for the configuration of a textual world describing reality or proposing a fictitious world, providing information about the type of text. In December 2020 our treebank comprised 42,542 sentences (919,608 words and punctuation). We studied our documents containing fictional and non-fictional narrative. Using a Malt parser optimizer, we extracted dependency chains of time and spatial complements. The number of complements and the degree to which they are precise is related to the type of text, fictional or nonfictional. In order to construct a classifier of texts, one can count the spatial and temporal complements and one can observe if they represent determinations of exact landmarks ( with geographical proper names and numbers) - in which case the text is a real narrative, or if they represent imprecise determinations, in which case the narrative is fictional.