Animacy Detection in Stories

F.B. Karsdorp, Marten van der Meulen, Theo Meder, Antal van den Bosch

Research output: Chapter in book/volumeContribution to conference proceedingsScientificpeer-review

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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.
Original languageEnglish
Title of host publicationProceedings of the Workshop on Computational Models of Narrative (CMN’15)
EditorsMark Finlayson, Ben Miller, Antonio Lieto, Remi Ronfard
Place of PublicationAtlanta
Pages82-97
Number of pages15
DOIs
Publication statusPublished - May 2015

Publication series

NameOpenAccess Series in Informatics
PublisherOASICS Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany

Keywords

  • animacy detection
  • word embedding
  • neural network
  • folktale
  • semantic mapping

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