Papers by Youngja Park
Supervising Unsupervised Open Information Extraction Models (D19-1)
Copied to clipboard
| Challenge: | Existing supervised methods that use labeled training data are expensive and difficult to adapt to new domains. |
| Approach: | They propose a supervised open information extraction framework that leverages unsupervised Open IE systems and labeled data to improve system performance. |
| Outcome: | The proposed method outperforms existing supervised and unsupervised models by a significant margin. |
Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications (2023.acl-short)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a foundational task for a variety of applications like question answering and machine translation. |
| Approach: | They propose to split entity recognition problem into two sub-tasks and optimize them separately for each sub-task. |
| Outcome: | The proposed system outperforms baselines on OntoNotes5.0, WNUT17 and a cybersecurity dataset and gives on-par performance on BioNLP13CG. |
A Pretrained Language Model for Cyber Threat Intelligence (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Earlier studies have shown that domain-specific LMs are crucial for domain-specific applications. |
| Approach: | They propose a new BERT model for the cybersecurity domain, CTI-BERT . they find it significantly outperforms general-domain and security-domain models . |
| Outcome: | The proposed model outperforms general-domain and security-domain models for cybersecurity tasks. |
Full-Stack Information Extraction System for Cybersecurity Intelligence (2022.emnlp-industry)
Copied to clipboard
| Challenge: | a rapid increase in cyberattacks and attacks pose enormous challenges to security analysts. |
| Approach: | They propose a full-stack information extraction system for the cybersecurity domain that extracts 26 entity types, 20 fixed relations and the temporal information of the relations. |
| Outcome: | The proposed system can extract 26 entity types, 20 fixed rela and temporal information of relations. |
Predicting Malware Attributes from Cybersecurity Texts (N19-1)
Copied to clipboard
| Challenge: | a new feature learning method is proposed to automatically assign malware attribute labels based on cybersecurity texts. |
| Approach: | They propose a feature learning method to leverage diverse knowledge sources to automatically assign malware attribute labels based on cybersecurity texts. |
| Outcome: | The proposed method outperforms the state-of-the-art malware attribute prediction systems. |