James Foulds


2019

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Scalable Collapsed Inference for High-Dimensional Topic Models
Rashidul Islam | James Foulds
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)

The bigger the corpus, the more topics it can potentially support. To truly make full use of massive text corpora, a topic model inference algorithm must therefore scale efficiently in 1) documents and 2) topics, while 3) achieving accurate inference. Previous methods have achieved two out of three of these criteria simultaneously, but never all three at once. In this paper, we develop an online inference algorithm for topic models which leverages stochasticity to scale well in the number of documents, sparsity to scale well in the number of topics, and which operates in the collapsed representation of the topic model for improved accuracy and run-time performance. We use a Monte Carlo inner loop in the online setting to approximate the collapsed variational Bayes updates in a sparse and efficient way, which we accomplish via the MetropolisHastings Walker method. We showcase our algorithm on LDA and the recently proposed mixed membership skip-gram topic model. Our method requires only amortized O(kd) computation per word token instead of O(K) operations, where the number of topics occurring for a particular document kd the total number of topics in the corpus K, to converge to a high-quality solution.

2015

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RELLY: Inferring Hypernym Relationships Between Relational Phrases
Adam Grycner | Gerhard Weikum | Jay Pujara | James Foulds | Lise Getoor
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums
Arti Ramesh | Shachi H. Kumar | James Foulds | Lise Getoor
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Joint Models of Disagreement and Stance in Online Debate
Dhanya Sridhar | James Foulds | Bert Huang | Lise Getoor | Marilyn Walker
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2013

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Modeling Scientific Impact with Topical Influence Regression
James Foulds | Padhraic Smyth
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing