Abstract
The public data on the Internet contains a large amount of high-value open source intelligence (OSINT) for the national defense. As the fundamental information extraction task, Named Entity Recognition (NER) plays a key role in question answering systems, knowledge graphs and reasoning. However, NER for the national defense domain achieves little progress due to unavailable datasets. Most previous methods mainly work on general-purpose datasets which lack insight into the particularity of the national defense. In this paper, we propose a Chinese NER dataset, ND-NER, for the national defense based on the data crawled from Sina Weibo. This is the first public human-annotation NER dataset for OSINT towards the national defense domain with 19 entity types and 418,227 tokens. We construct two baseline tasks and implement a series of popular models on our dataset. The empirical results show that ND-NER is a challenging dataset concerning the long entities with the nest structure, domain specialization, ambiguous entity boundaries, informality and colloquialism issues of social media. We believe that the published ND-NER at https://github.com/XinyanLi2016/ND-NER will encourage further exploring for OSINT towards the national defense domain.
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This work is supported by the National Key Research and Development Program (2019YFB2102600).
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Li, X., Li, D., Yang, Z., Zhao, H., Cai, W., Lin, X. (2023). ND-NER: A Named Entity Recognition Dataset for OSINT Towards the National Defense Domain. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1792. Springer, Singapore. https://doi.org/10.1007/978-981-99-1642-9_31
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DOI: https://doi.org/10.1007/978-981-99-1642-9_31
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