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
- Editors:
- Erhard Hinrichs, Marie Hinrichs, Thorsten Trippel
- Venue:
- LT4DH
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 18–25
- Language:
- URL:
- https://aclanthology.org/W16-4004
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W16-4004.pdf