UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers
Matt Magnusson, Laura Dietz · 2019
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 stateof-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.