Evaluation of Word Embeddings for the Social Sciences
- URL: http://arxiv.org/abs/2302.06174v1
- Date: Mon, 13 Feb 2023 08:23:03 GMT
- Title: Evaluation of Word Embeddings for the Social Sciences
- Authors: Ricardo Schiffers, Dagmar Kern, Daniel Hienert
- Abstract summary: We describe the creation and evaluation of word embedding models based on 37,604 social science research papers.
We found that the created domain-specific model covers a large part of social science concepts.
Across all relation types, we found a more extensive coverage of semantic relationships.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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