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    <abstract>This paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP. The DM_NLP participated in two subtasks: SubTask 1 classifies if a sentence is useful for inferring malware actions and capabilities, and SubTask 2 predicts token labels (“Action”, “Entity”, “Modifier” and “Others”) for a given malware-related sentence. Since we leverage results of Subtask 2 directly to infer the result of Subtask 1, the paper focus on the system solving Subtask 2. By taking Subtask 2 as a sequence labeling task, our system relies on a recurrent neural network named BiLSTM-CNN-CRF with rich linguistic features, such as POS tags, dependency parsing labels, chunking labels, NER labels, Brown clustering. Our system achieved the highest F1 score in both token level and phrase level.</abstract>
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%0 Conference Proceedings
%T DM_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features
%A Ma, Chunping
%A Zheng, Huafei
%A Xie, Pengjun
%A Li, Chen
%A Li, Linlin
%A Si, Luo
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F ma-etal-2018-dm
%X This paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP. The DM_NLP participated in two subtasks: SubTask 1 classifies if a sentence is useful for inferring malware actions and capabilities, and SubTask 2 predicts token labels (“Action”, “Entity”, “Modifier” and “Others”) for a given malware-related sentence. Since we leverage results of Subtask 2 directly to infer the result of Subtask 1, the paper focus on the system solving Subtask 2. By taking Subtask 2 as a sequence labeling task, our system relies on a recurrent neural network named BiLSTM-CNN-CRF with rich linguistic features, such as POS tags, dependency parsing labels, chunking labels, NER labels, Brown clustering. Our system achieved the highest F1 score in both token level and phrase level.
%R 10.18653/v1/S18-1114
%U https://aclanthology.org/S18-1114
%U https://doi.org/10.18653/v1/S18-1114
%P 707-711

Markdown (Informal)

[DM_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features](https://aclanthology.org/S18-1114) (Ma et al., SemEval 2018)

• DM_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features (Ma et al., SemEval 2018)

ACL

• Chunping Ma, Huafei Zheng, Pengjun Xie, Chen Li, Linlin Li, and Luo Si. 2018. DM_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 707–711, New Orleans, Louisiana. Association for Computational Linguistics.