# Evaluation of Word Embeddings for the Social Sciences

arXiv: https://arxiv.org/abs/2302.06174 · HTML: https://mlsanity.com/paper/2302.06174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts](https://mlsanity.com/paper/1709.07470/) (2017)
- [Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents](https://mlsanity.com/paper/2206.10939/) (2022)
- [Word Embedding for Social Sciences: An Interdisciplinary Survey](https://mlsanity.com/paper/2207.03086/) (2022)

## Abstract (arXiv)

Word embeddings are an essential instrument in many NLP tasks. Most available resources are trained on general language from Web corpora or Wikipedia dumps. However, word embeddings for domain-specific language are rare, in particular for the social science domain. Therefore, in this work, we describe the creation and evaluation of word embedding models based on 37,604 open-access social science research papers. In the evaluation, we compare domain-specific and general language models for (i) language coverage, (ii) diversity, and (iii) semantic relationships. We found that the created domain-specific model, even with a relatively small vocabulary size, covers a large part of social science concepts, their neighborhoods are diverse in comparison to more general models. Across all relation types, we found a more extensive coverage of semantic relationships.