# Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word Embeddings

## TL;DR

This paper introduces high-quality relation extraction datasets in the digital humanities, evaluates various word embedding models on these datasets, and analyzes factors affecting model performance in small corpus scenarios.

## Contribution

It presents new datasets for relation extraction in digital humanities and systematically evaluates multiple word embedding models on these datasets, providing insights into their effectiveness.

## Key findings

- Word embedding models perform variably on small, domain-specific datasets.
- Corpus term frequency and task difficulty significantly impact model accuracy.
- The datasets and evaluation framework are publicly available for further research.

## Abstract

In this research, we manually create high-quality datasets in the digital humanities domain for the evaluation of language models, specifically word embedding models. The first step comprises the creation of unigram and n-gram datasets for two fantasy novel book series for two task types each, analogy and doesn't-match. This is followed by the training of models on the two book series with various popular word embedding model types such as word2vec, GloVe, fastText, or LexVec. Finally, we evaluate the suitability of word embedding models for such specific relation extraction tasks in a situation of comparably small corpus sizes. In the evaluations, we also investigate and analyze particular aspects such as the impact of corpus term frequencies and task difficulty on accuracy. The datasets, and the underlying system and word embedding models are available on github and can be easily extended with new datasets and tasks, be used to reproduce the presented results, or be transferred to other domains.