Extracting Social Networks from Literary Text with Word Embedding Tools
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.
LT4DH@COLING