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        <title>UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers</title>
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            <namePart type="given">Jonathan</namePart>
            <namePart type="family">May</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers
%A Magnusson, Matthew
%A Dietz, Laura
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F magnusson-dietz-2019-unh
%X 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.
%R 10.18653/v1/S19-2230
%U https://aclanthology.org/S19-2230
%U https://doi.org/10.18653/v1/S19-2230
%P 1308-1312

Markdown (Informal)

[UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers](https://aclanthology.org/S19-2230) (Magnusson & Dietz, SemEval 2019)

• UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers (Magnusson & Dietz, SemEval 2019)

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.