@inbook{2b5893f97b354a96a3002f22d46b550d,
title = "Animacy Detection in Stories",
abstract = "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.",
keywords = "animacy detection, word embedding, neural network, folktale, semantic mapping",
author = "F.B. Karsdorp and {van der Meulen}, Marten and Theo Meder and {van den Bosch}, Antal",
year = "2015",
month = may,
doi = "10.4230/OASIcs.CMN.2015.82",
language = "English",
series = "OpenAccess Series in Informatics",
publisher = "OASICS Schloss Dagstuhl – Leibniz-Zentrum f{\"u}r Informatik, Dagstuhl Publishing, Germany",
pages = "82--97",
editor = "Mark Finlayson and Ben Miller and Antonio Lieto and Remi Ronfard",
booktitle = "Proceedings of the Workshop on Computational Models of Narrative (CMN{\textquoteright}15)",
}