Abstract
The SemEval-2019 Task 12 is toponym resolution in scientific papers. We focus on Subtask 1: Toponym Detection which is the identification of spans of text for place names mentioned in a document. We propose two methods: 1) sliding window convolutional neural network using ELMo embeddings (cnn-elmo), and 2) sliding window multi-Layer perceptron using ELMo embeddings (mlp-elmo). We also submit Bi-lateral LSTM with Conditional Random Fields (bi-LSTM) as a strong baseline given its state-of-art performance in Named Entity Recognition (NER) task. Our best performing model is cnn-elmo with a F1 of 0.844 which was below bi-LSTM F1 of 0.862 when evaluated on overlap macro detection. Eight teams participated in this subtask with a total of 21 submissions.- Anthology ID:
- S19-2230
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1308–1312
- Language:
- URL:
- https://aclanthology.org/S19-2230
- DOI:
- 10.18653/v1/S19-2230
- Cite (ACL):
- Matthew Magnusson and Laura Dietz. 2019. UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1308–1312, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers (Magnusson & Dietz, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/S19-2230.pdf