Animacy Detection in Stories
Folgert Karsdorp, Marten van der Meulen, T. Meder, Antal van den Bosch · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2015
This paper presents a linguistically uninformed computational model for animacy classification. The model makes use of word n-grams in combination with lower dimensional word embedding representations that are learned from a web-scale corpus. We compare the model to a number of linguistically informed models that use features such as dependency tags and show competitive results. We apply our animacy classifier to a large collection of Dutch folktales to obtain a list of all characters in the stories. We then draw a semantic map of all automatically extracted characters which provides a unique entrance point to the collection.