Documents

  • paper2-2

    Final published version, 678 KB, PDF-document

DOI

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 book/volumeProceedings 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
DOI
StatePublished - May 2015

Publication series

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

    Research areas

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

ID: 1057285