457 citations · 505 across the 9 of their papers we have counts for
9 papers
User Factor Adaptation for User Embedding via Multitask Learning
Xiaolei Huang, Michael J. Paul, Robin Burke +2
Language varies across users and their interested fields in social media data: words authored by a user across his/her interests may have different meanings (e.g., cool) or sentime…
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries
Mozhi Zhang, Yoshinari Fujinuma, Michael J. Paul +1
Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI). Recent CLWE methods use linear projections, which underfit the training dictionary, t…
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
Xiaolei Huang, Linzi Xing, Franck Dernoncourt +1
Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. In this wo…
Evaluating Topic Quality with Posterior Variability
Linzi Xing, Michael J. Paul, Giuseppe Carenini
Probabilistic topic models such as latent Dirichlet allocation (LDA) are popularly used with Bayesian inference methods such as Gibbs sampling to learn posterior distributions over…
A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity
Yoshinari Fujinuma, Jordan Boyd-Graber, Michael J. Paul
Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. An important requirement for many downstream tasks is that w…
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models
Shudong Hao, Michael J. Paul
Probabilistic topic modeling is a popular choice as the first step of crosslingual tasks to enable knowledge transfer and extract multilingual features. While many multilingual top…
Learning Multilingual Topics from Incomparable Corpus
Shudong Hao, Michael J. Paul
Multilingual topic models enable crosslingual tasks by extracting consistent topics from multilingual corpora. Most models require parallel or comparable training corpora, which li…
Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation
Shudong Hao, Jordan Boyd-Graber, Michael J. Paul
Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data. However, there is no standard and effective metric to evalu…
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
Mauricio Santillana, Andre T. Nguyen, Mark Dredze +2
We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple d…