Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection
Author:
Affiliation:
1. Institute of Information and Communication Technologies, Bulgarian Academy of Science, Bulgaria
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3660853.3660878
Reference14 articles.
1. Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text
2. Unsupervised Cross-lingual Representation Learning at Scale
3. LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
4. Ri, R., Yamada, I., & Tsuruoka, Y. (2022). mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models. arXiv. arXiv:2110.08151v3 [cs.CL]
5. Li, Z., Zhou, W., Chiang, Y.-Y., & Chen, M. (2023). GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding. arXiv. arXiv:2310.14478v1 [cs.CL]
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