@inproceedings{wang-etal-2019-dm,
title = "{DM}{\_}{NLP} at {S}em{E}val-2018 Task 12: A Pipeline System for Toponym Resolution",
author = "Wang, Xiaobin and
Ma, Chunping and
Zheng, Huafei and
Liu, Chu and
Xie, Pengjun and
Li, Linlin and
Si, Luo",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2156",
doi = "10.18653/v1/S19-2156",
pages = "917--923",
abstract = "This paper describes DM-NLP{'}s system for toponym resolution task at Semeval 2019. Our system was developed for toponym detection, disambiguation and end-to-end resolution which is a pipeline of the former two. For toponym detection, we utilized the state-of-the-art sequence labeling model, namely, BiLSTM-CRF model as backbone. A lot of strategies are adopted for further improvement, such as pre-training, model ensemble, model averaging and data augment. For toponym disambiguation, we adopted the widely used searching and ranking framework. For ranking, we proposed several effective features for measuring the consistency between the detected toponym and toponyms in GeoNames. Eventually, our system achieved the best performance among all the submitted results in each sub task.",
}
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<abstract>This paper describes DM-NLP’s system for toponym resolution task at Semeval 2019. Our system was developed for toponym detection, disambiguation and end-to-end resolution which is a pipeline of the former two. For toponym detection, we utilized the state-of-the-art sequence labeling model, namely, BiLSTM-CRF model as backbone. A lot of strategies are adopted for further improvement, such as pre-training, model ensemble, model averaging and data augment. For toponym disambiguation, we adopted the widely used searching and ranking framework. For ranking, we proposed several effective features for measuring the consistency between the detected toponym and toponyms in GeoNames. Eventually, our system achieved the best performance among all the submitted results in each sub task.</abstract>
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%0 Conference Proceedings
%T DM_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution
%A Wang, Xiaobin
%A Ma, Chunping
%A Zheng, Huafei
%A Liu, Chu
%A Xie, Pengjun
%A Li, Linlin
%A Si, Luo
%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 wang-etal-2019-dm
%X This paper describes DM-NLP’s system for toponym resolution task at Semeval 2019. Our system was developed for toponym detection, disambiguation and end-to-end resolution which is a pipeline of the former two. For toponym detection, we utilized the state-of-the-art sequence labeling model, namely, BiLSTM-CRF model as backbone. A lot of strategies are adopted for further improvement, such as pre-training, model ensemble, model averaging and data augment. For toponym disambiguation, we adopted the widely used searching and ranking framework. For ranking, we proposed several effective features for measuring the consistency between the detected toponym and toponyms in GeoNames. Eventually, our system achieved the best performance among all the submitted results in each sub task.
%R 10.18653/v1/S19-2156
%U https://aclanthology.org/S19-2156
%U https://doi.org/10.18653/v1/S19-2156
%P 917-923
Markdown (Informal)
[DM_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution](https://aclanthology.org/S19-2156) (Wang et al., SemEval 2019)
ACL
- Xiaobin Wang, Chunping Ma, Huafei Zheng, Chu Liu, Pengjun Xie, Linlin Li, and Luo Si. 2019. DM_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 917–923, Minneapolis, Minnesota, USA. Association for Computational Linguistics.