UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers

Matthew Magnusson, Laura Dietz
2019 Proceedings of the 13th International Workshop on Semantic Evaluation  
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 a bi-directional LSTM with Conditional Random Fields (bi-LSTM) as a strong baseline given its
more » ... 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.
doi:10.18653/v1/s19-2230 dblp:conf/semeval/MagnussonD19 fatcat:iu3i3k46ujgzxhw2c2gxcnhpaq