Extracting Social Networks from Literary Text with Word Embedding Tools

Gerhard Wohlgenannt, Ekaterina Chernyak, Dmitry Ilvovsky


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.
Anthology ID:
W16-4004
Volume:
Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
LT4DH
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
18–25
Language:
URL:
https://aclanthology.org/W16-4004
DOI:
Bibkey:
Cite (ACL):
Gerhard Wohlgenannt, Ekaterina Chernyak, and Dmitry Ilvovsky. 2016. Extracting Social Networks from Literary Text with Word Embedding Tools. In Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH), pages 18–25, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Extracting Social Networks from Literary Text with Word Embedding Tools (Wohlgenannt et al., LT4DH 2016)
Copy Citation:
PDF:
https://aclanthology.org/W16-4004.pdf