UMBC at SemEval-2018 Task 8: Understanding Text about Malware

Padia, Ankur; Roy, Arpıta; Satyapanich, Taneeya W.; Ferraro, Francis; Pan, Shimei; Park, Youngja; Joshi, Anupam; Finin, Tim

doi:10.18653/v1/s18-1142

Cited by 9 publications

(5 citation statements)

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“…The input of our dataset was in a text format with positive and negative labels. Therefore, we chose four baselines that are capable of handling text dataset: NBC [ 33 ], SVM [ 34 ], MLP [ 35 ], and CNN n-gram [ 36 ]. NBC and SVM are considered as traditional machine learning algorithms.…”

Section: Methodsmentioning

confidence: 99%

A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter

Ismail

Liu

et al. 2020

BMC Med Inform Decis Mak

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Background Emotions after surviving cancer can be complicated. The survivors may have gained new strength to continue life, but some of them may begin to deal with complicated feelings and emotional stress due to trauma and fear of cancer recurrence. The widespread use of Twitter for socializing has been the alternative medium for data collection compared to traditional studies of mental health, which primarily depend on information taken from medical staff with their consent. These social media data, to a certain extent, reflect the users’ psychological state. However, Twitter also contains a mix of noisy and genuine tweets. The process of manually identifying genuine tweets is expensive and time-consuming. Methods We stream the data using cancer as a keyword to filter the tweets with cancer-free and use post-traumatic stress disorder (PTSD) related keywords to reduce the time spent on the annotation task. Convolutional Neural Network (CNN) learns the representations of the input to identify cancer survivors with PTSD. Results The results present that the proposed CNN can effectively identify cancer survivors with PTSD. The experiments on real-world datasets show that our model outperforms the baselines and correctly classifies the new tweets. Conclusions PTSD is one of the severe anxiety disorders that could affect individuals who are exposed to traumatic events, including cancer. Cancer survivors are at risk of short-term or long-term effects on physical and psycho-social well-being. Therefore, the evaluation and treatment of PTSD are essential parts of cancer survivorship care. It will act as an alarming system by detecting the PTSD presence based on users’ postings on Twitter.

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Section: Methodsmentioning

confidence: 99%

A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter

Ismail

Liu

et al. 2020

BMC Med Inform Decis Mak

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“…The vocabulary size of the Domain-word2vec embedding was 28,283 words. The Cyber-Word2vec embeddings were produced by Padia et al (2018). It has 100dimension which trained on a large corpus of approximately one million cybersecurity-related webpages.…”

Section: Event Nugget and Event Argument Detectionmentioning

confidence: 99%

CASIE: Extracting Cybersecurity Event Information from Text

Satyapanich

Ferraro

Finin

2020

AAAI

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We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. It was trained on a new corpus of 1,000 English news articles from 2017–2019 that are labeled with rich, event-based annotations and that covers both cyberattack and vulnerability-related events. Our model defines five event subtypes along with their semantic roles and 20 event-relevant argument types (e.g., file, device, software, money). CASIE uses different deep neural networks approaches with attention and can incorporate rich linguistic features and word embeddings. We have conducted experiments on each component in the event detection pipeline and the results show that each subsystem performs well.

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“…TeamDL [15] built a convolutional neural network with original glove embeddings. UMBC [16] used a Multilayer Perceptron model for the submission of SubTask 1. Inspired from the tasks, Ravikiran [17] has proposed a multimodal dataset with QR-codes and Malware Text classification.…”

Section: Related Workmentioning

confidence: 99%