Youngja Park


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Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries
Wenjie Wang | Youngja Park | Taesung Lee | Ian Molloy | Pengfei Tang | Li Xiong
Proceedings of the 3rd Clinical Natural Language Processing Workshop

Recent studies have shown that adversarial examples can be generated by applying small perturbations to the inputs such that the well- trained deep learning models will misclassify. With the increasing number of safety and security-sensitive applications of deep learn- ing models, the robustness of deep learning models has become a crucial topic. The robustness of deep learning models for health- care applications is especially critical because the unique characteristics and the high financial interests of the medical domain make it more sensitive to adversarial attacks. Among the modalities of medical data, the clinical summaries have higher risks to be attacked because they are generated by third-party companies. As few works studied adversarial threats on clinical summaries, in this work we first apply adversarial attack to clinical summaries of electronic health records (EHR) to show the text-based deep learning systems are vulnerable to adversarial examples. Secondly, benefiting from the multi-modality of the EHR dataset, we propose a novel defense method, MATCH (Multimodal feATure Consistency cHeck), which leverages the consistency between multiple modalities in the data to defend against adversarial examples on a single modality. Our experiments demonstrate the effectiveness of MATCH on a hospital readmission prediction task comparing with baseline methods.


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Supervising Unsupervised Open Information Extraction Models
Arpita Roy | Youngja Park | Taesung Lee | Shimei Pan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a novel supervised open information extraction (Open IE) framework that leverages an ensemble of unsupervised Open IE systems and a small amount of labeled data to improve system performance. It uses the outputs of multiple unsupervised Open IE systems plus a diverse set of lexical and syntactic information such as word embedding, part-of-speech embedding, syntactic role embedding and dependency structure as its input features and produces a sequence of word labels indicating whether the word belongs to a relation, the arguments of the relation or irrelevant. Comparing with existing supervised Open IE systems, our approach leverages the knowledge in existing unsupervised Open IE systems to overcome the problem of insufficient training data. By employing multiple unsupervised Open IE systems, our system learns to combine the strength and avoid the weakness in each individual Open IE system. We have conducted experiments on multiple labeled benchmark data sets. Our evaluation results have demonstrated the superiority of the proposed method over existing supervised and unsupervised models by a significant margin.

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Predicting Malware Attributes from Cybersecurity Texts
Arpita Roy | Youngja Park | Shimei Pan
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Text analytics is a useful tool for studying malware behavior and tracking emerging threats. The task of automated malware attribute identification based on cybersecurity texts is very challenging due to a large number of malware attribute labels and a small number of training instances. In this paper, we propose a novel feature learning method to leverage diverse knowledge sources such as small amount of human annotations, unlabeled text and specifications about malware attribute labels. Our evaluation has demonstrated the effectiveness of our method over the state-of-the-art malware attribute prediction systems.


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UMBC at SemEval-2018 Task 8: Understanding Text about Malware
Ankur Padia | Arpita Roy | Taneeya Satyapanich | Francis Ferraro | Shimei Pan | Youngja Park | Anupam Joshi | Tim Finin
Proceedings of The 12th International Workshop on Semantic Evaluation

We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) classifying sentences as being relevant or irrelevant to malware, (2) predicting token labels for sentences, and (4) predicting attribute labels from the Malware Attribute Enumeration and Characterization vocabulary for defining malware characteristics. We achieve F1 score of 50.34/18.0 (dev/test), 22.23 (test-data), and 31.98 (test-data) for Task1, Task2 and Task2 respectively. We also make our cybersecurity embeddings publicly available at


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Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning
Hiroshi Kanayama | Youngja Park | Yuta Tsuboi | Dongmook Yi
Proceedings of the EMNLP’2014 Workshop on Language Technology for Closely Related Languages and Language Variants


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Extracting Salient Keywords from Instructional Videos Using Joint Text, Audio and Visual Cues
Youngja Park | Ying Li
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers


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Automatic Glossary Extraction: Beyond Terminology Identification
Youngja Park | Roy J. Byrd | Branimir K. Boguraev
COLING 2002: The 19th International Conference on Computational Linguistics

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Identification of Probable Real Words: An Entropy-based Approach
Youngja Park
Proceedings of the ACL-02 Workshop on Unsupervised Lexical Acquisition


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Hybrid Text Mining for Finding Abbreviations and their Definitions
Youngja Park | Roy J. Byrd
Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing