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

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 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.

TIMBERT: Toponym Identifier For The Medical Domain Based on BERT

“…Network Architecture There are two paradigms governing the network architectures used for this task: namely, whether localized contextual information is enough, or all available contextual information should be taken into account when making predictions. The former leads to models that only have access to a sliding window of information such as CNNs or MLPs (Magge et al, 2018;Davari et al, 2019;Magnusson and Dietz, 2019). The latter leads to sequential models operating at the sentence level; among which the BiLSTM-CRF architecture is the most favored and provides state-of-the-art results Yadav et al, 2019;Qi et al, 2019;Magnusson and Dietz, 2019).…”

“…The former leads to models that only have access to a sliding window of information such as CNNs or MLPs (Magge et al, 2018;Davari et al, 2019;Magnusson and Dietz, 2019). The latter leads to sequential models operating at the sentence level; among which the BiLSTM-CRF architecture is the most favored and provides state-of-the-art results Yadav et al, 2019;Qi et al, 2019;Magnusson and Dietz, 2019). In our experiments, we focused on neural architectures that considered all available contextual information within a sentence.…”