Aldrian Obaja Muis


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Analyzing Incorporation of Emotion in Emoji Prediction
Shirley Anugrah Hayati | Aldrian Obaja Muis
Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In this work, we investigate the impact of incorporating emotion classes on the task of predicting emojis from Twitter texts. More specifically, we first show that there is a correlation between the emotion expressed in the text and the emoji choice of Twitter users. Based on this insight we propose a few simple methods to incorporate emotion information in traditional classifiers. Through automatic metrics, human evaluation, and error analysis, we show that the improvement obtained by incorporating emotion is significant and correlate better with human preferences compared to the baseline models. Through the human ratings that we obtained, we also argue for preference metric to better evaluate the usefulness of an emoji prediction system.


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Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort
Aldrian Obaja Muis | Naoki Otani | Nidhi Vyas | Ruochen Xu | Yiming Yang | Teruko Mitamura | Eduard Hovy
Proceedings of the 27th International Conference on Computational Linguistics

The use of machine learning for NLP generally requires resources for training. Tasks performed in a low-resource language usually rely on labeled data in another, typically resource-rich, language. However, there might not be enough labeled data even in a resource-rich language such as English. In such cases, one approach is to use a hand-crafted approach that utilizes only a small bilingual dictionary with minimal manual verification to create distantly supervised data. Another is to explore typical machine learning techniques, for example adversarial training of bilingual word representations. We find that in event-type detection task—the task to classify [parts of] documents into a fixed set of labels—they give about the same performance. We explore ways in which the two methods can be complementary and also see how to best utilize a limited budget for manual annotation to maximize performance gain.


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Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators
Aldrian Obaja Muis | Wei Lu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In this paper, we propose a new model that is capable of recognizing overlapping mentions. We introduce a novel notion of mention separators that can be effectively used to capture how mentions overlap with one another. On top of a novel multigraph representation that we introduce, we show that efficient and exact inference can still be performed. We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences. Through extensive empirical analysis on standard datasets, we demonstrate the effectiveness of our approach.

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MalwareTextDB: A Database for Annotated Malware Articles
Swee Kiat Lim | Aldrian Obaja Muis | Wei Lu | Chen Hui Ong
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Cybersecurity risks and malware threats are becoming increasingly dangerous and common. Despite the severity of the problem, there has been few NLP efforts focused on tackling cybersecurity. In this paper, we discuss the construction of a new database for annotated malware texts. An annotation framework is introduced based on the MAEC vocabulary for defining malware characteristics, along with a database consisting of 39 annotated APT reports with a total of 6,819 sentences. We also use the database to construct models that can potentially help cybersecurity researchers in their data collection and analytics efforts.


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Weak Semi-Markov CRFs for Noun Phrase Chunking in Informal Text
Aldrian Obaja Muis | Wei Lu
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Learning to Recognize Discontiguous Entities
Aldrian Obaja Muis | Wei Lu
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing