@inproceedings{loyola-etal-2018-villani,
title = "Villani at {S}em{E}val-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning",
author = "Loyola, Pablo and
Gajananan, Kugamoorthy and
Watanabe, Yuji and
Satoh, Fumiko",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1143",
doi = "10.18653/v1/S18-1143",
pages = "885--889",
abstract = "In this paper, we describe our proposal for the task of Semantic Extraction from Cybersecurity Reports. The goal is to explore if natural language processing methods can provide relevant and actionable knowledge to contribute to better understand malicious behavior. Our method consists of an attention-based Bi-LSTM which achieved competitive performance of 0.57 for the Subtask 1. In the due process we also present ablation studies across multiple embeddings and their level of representation and also report the strategies we used to mitigate the extreme imbalance between classes.",
}
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%0 Conference Proceedings
%T Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning
%A Loyola, Pablo
%A Gajananan, Kugamoorthy
%A Watanabe, Yuji
%A Satoh, Fumiko
%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 loyola-etal-2018-villani
%X In this paper, we describe our proposal for the task of Semantic Extraction from Cybersecurity Reports. The goal is to explore if natural language processing methods can provide relevant and actionable knowledge to contribute to better understand malicious behavior. Our method consists of an attention-based Bi-LSTM which achieved competitive performance of 0.57 for the Subtask 1. In the due process we also present ablation studies across multiple embeddings and their level of representation and also report the strategies we used to mitigate the extreme imbalance between classes.
%R 10.18653/v1/S18-1143
%U https://aclanthology.org/S18-1143
%U https://doi.org/10.18653/v1/S18-1143
%P 885-889
Markdown (Informal)
[Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning](https://aclanthology.org/S18-1143) (Loyola et al., SemEval 2018)
ACL