@inproceedings{wohlgenannt-etal-2016-extracting,
    title = "Extracting Social Networks from Literary Text with Word Embedding Tools",
    author = "Wohlgenannt, Gerhard  and
      Chernyak, Ekaterina  and
      Ilvovsky, Dmitry",
    editor = "Hinrichs, Erhard  and
      Hinrichs, Marie  and
      Trippel, Thorsten",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/landing_page/W16-4004/",
    pages = "18--25",
    abstract = "In this paper a social network is extracted from a literary text. The social network shows, how frequent the characters interact and how similar their social behavior is. Two types of similarity measures are used: the first applies co-occurrence statistics, while the second exploits cosine similarity on different types of word embedding vectors. The results are evaluated by a paid micro-task crowdsourcing survey. The experiments suggest that specific types of word embeddings like word2vec are well-suited for the task at hand and the specific circumstances of literary fiction text."
}