Shimei Pan


2019

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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.

2018

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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 http://bit.ly/cyber2vec.

2017

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Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction
Tao Ding | Warren K. Bickel | Shimei Pan
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain on a large scale, to maximize system performance, we explore different unsupervised feature learning methods to take advantage of a large amount of unsupervised social media data. We also demonstrate the benefit of using multi-view unsupervised feature learning to combine heterogeneous user information such as Facebook “likes” and “status updates” to enhance system performance. Based on our evaluation, our best models achieved 86% AUC for predicting tobacco use, 81% for alcohol use and 84% for illicit drug use, all of which significantly outperformed existing methods. Our investigation has also uncovered interesting relations between a user’s social media behavior (e.g., word usage) and substance use.

2016

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Personalized Emphasis Framing for Persuasive Message Generation
Tao Ding | Shimei Pan
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Using Personal Traits For Brand Preference Prediction
Chao Yang | Shimei Pan | Jalal Mahmud | Huahai Yang | Padmini Srinivasan
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Active Learning with Constrained Topic Model
Yi Yang | Shimei Pan | Doug Downey | Kunpeng Zhang
Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces

2005

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Instance-based Sentence Boundary Determination by Optimization for Natural Language Generation
Shimei Pan | James Shaw
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2002

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Designing a Speech Corpus for Instance-based Spoken Language Generation
Shimei Pan | Wubin Weng
Proceedings of the International Natural Language Generation Conference

2000

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Modeling Local Context for Pitch Accent Prediction
Shimei Pan | Julia Hirschberg
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

1999

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Word Informativeness and Automatic Pitch Accent Modeling
Shimei Pan | Kathleen R. McKeown
1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

1998

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Evaluating Response Strategies in a Web-Based Spoken Dialogue Agent
Diane J. Litman | Shimei Pan | Marilyn A. Walker
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Learning Intonation Rules for Concept to Speech Generation
Shimei Pan | Kathleen McKeown
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Evaluating Response Strategies in a Web-Based Spoken Dialogue Agent
Diane J. Litman | Shimei Pan | Marilyn A. Walker
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Learning Intonation Rules for Concept to Speech Generation
Shimei Pan | Kathleen McKeown
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

1997

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Language Generation for Multimedia Healthcare Briefings
Kathleen R. McKeown | Desmond A. Jordan | Shimei Pan | James Shaw | Barry A. Allen
Fifth Conference on Applied Natural Language Processing

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Integrating Language Generation with Speech Synthesis in a Concept to Speech System
Shimei Pan | Kathleen R. McKeown
Concept to Speech Generation Systems

1992

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Knowledge Acquisition and Chinese Parsing Based on Corpus
Chunfa Yuan | Changning Huang | Shimei Pan
COLING 1992 Volume 4: The 14th International Conference on Computational Linguistics