Abstract
We evaluate the use of direct intrinsic word embedding evaluation tasks for specialized language. Our case study is philosophical text: human expert judgements on the relatedness of philosophical terms are elicited using a synonym detection task and a coherence task. Uniquely for our task, experts must rely on explicit knowledge and cannot use their linguistic intuition, which may differ from that of the philosopher. We find that inter-rater agreement rates are similar to those of more conventional semantic annotation tasks, suggesting that these tasks can be used to evaluate word embeddings of text types for which implicit knowledge may not suffice.- Anthology ID:
- 2021.humeval-1.12
- Volume:
- Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Anya Belz, Shubham Agarwal, Yvette Graham, Ehud Reiter, Anastasia Shimorina
- Venue:
- HumEval
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 107–113
- Language:
- URL:
- https://aclanthology.org/2021.humeval-1.12
- DOI:
- Cite (ACL):
- Goya van Boven and Jelke Bloem. 2021. Eliciting Explicit Knowledge From Domain Experts in Direct Intrinsic Evaluation of Word Embeddings for Specialized Domains. In Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval), pages 107–113, Online. Association for Computational Linguistics.
- Cite (Informal):
- Eliciting Explicit Knowledge From Domain Experts in Direct Intrinsic Evaluation of Word Embeddings for Specialized Domains (van Boven & Bloem, HumEval 2021)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.humeval-1.12.pdf
- Code
- gvanboven/direct-intrinsic-evaluation