Weiwei Yang


2019

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A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability
Weiwei Yang | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Multilingual topic models (MTMs) learn topics on documents in multiple languages. Past models align topics across languages by implicitly assuming the documents in different languages are highly comparable, often a false assumption. We introduce a new model that does not rely on this assumption, particularly useful in important low-resource language scenarios. Our MTM learns weighted topic links and connects cross-lingual topics only when the dominant words defining them are similar, outperforming LDA and previous MTMs in classification tasks using documents’ topic posteriors as features. It also learns coherent topics on documents with low comparability.

2017

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Adapting Topic Models using Lexical Associations with Tree Priors
Weiwei Yang | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Models work best when they are optimized taking into account the evaluation criteria that people care about. For topic models, people often care about interpretability, which can be approximated using measures of lexical association. We integrate lexical association into topic optimization using tree priors, which provide a flexible framework that can take advantage of both first order word associations and the higher-order associations captured by word embeddings. Tree priors improve topic interpretability without hurting extrinsic performance.

2016

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A Discriminative Topic Model using Document Network Structure
Weiwei Yang | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors
Weiwei Yang | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing