Word embeddings are already well studied in the general domain, usually trained on large text corpora, and have been evaluated for example on word similarity and analogy tasks, but also as an input to downstream NLP processes. In contrast, in this work we explore the suitability of word embedding technologies in the specialized digital humanities domain. After training embedding models of various types on two popular fantasy novel book series, we evaluate their performance on two task types: term analogies, and word intrusion. To this end, we manually construct test datasets with domain experts. Among the contributions are the evaluation of various word embedding techniques on the different task types, with the findings that even embeddings trained on small corpora perform well for example on the word intrusion task. Furthermore, we provide extensive and high-quality datasets in digital humanities for further investigation, as well as the implementation to easily reproduce or extend the experiments.
The offset method for solving word analo- gies has become a standard evaluation tool for vector-space semantic models: it is considered desirable for a space to repre- sent semantic relations as consistent vec- tor offsets. We show that the method's re- liance on cosine similarity conflates offset consistency with largely irrelevant neigh- borhood structure, and propose simple baselines that should be used to improve the utility of the method in vector space evaluation.