@InProceedings{ma-EtAl:2018:S18-1,
  author    = {Ma, Chunping  and  Zheng, Huafei  and  Xie, Pengjun  and  Li, Chen  and  Li, Linlin  and  Si, Luo},
  title     = {DM\_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {707--711},
  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.},
  url       = {http://www.aclweb.org/anthology/S18-1114}
}