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Full-Text Articles in Physical Sciences and Mathematics
Bayesian Modeling Of Dependency Trees Using Hierarchical Pitman-Yor Priors, Hanna Wallach, Charles Sutton, Andrew Mccallum
Bayesian Modeling Of Dependency Trees Using Hierarchical Pitman-Yor Priors, Hanna Wallach, Charles Sutton, Andrew Mccallum
Hanna M. Wallach
In this paper, we introduce two hierarchical Bayesian dependency parsing models. First, we show that a classic dependency parser can be substantially improved by (a) using a hierarchical Pitman-Yor process prior over the distribution over dependents of a word, and (b) sampling the model hyperparameters. Second, we present a parsing model in which latent state variables mediate the relationships between words and their dependents. The model clusters dependencies into states using a similar approach to that used by Bayesian topic models when clustering words into topics. The inferred states have a syntactic character, and lead to modestly improved parse accuracy …
Bayesian Modeling Of Dependency Trees Using Hierarchical Pitman-Yor Priors, Hanna Wallach, Charles Sutton, Andrew Mccallum
Bayesian Modeling Of Dependency Trees Using Hierarchical Pitman-Yor Priors, Hanna Wallach, Charles Sutton, Andrew Mccallum
Hanna M. Wallach
In this paper, we introduce two hierarchical Bayesian dependency parsing models. First, we show that a classic dependency parser can be substantially improved by (a) using a hierarchical Pitman-Yor process prior over the distribution over dependents of a word, and (b) sampling the model hyperparameters. Second, we present a parsing model in which latent state variables mediate the relationships between words and their dependents. The model clusters dependencies into states using a similar approach to that used by Bayesian topic models when clustering words into topics. The inferred states have a syntactic character, and lead to modestly improved parse accuracy …
Intelligent Email: Aiding Users With Ai, Mark Dredze, Hanna Wallach, Danny Puller, Tova Brooks, Josh Carroll, Joshua Magarick, John Blitzer, Fernando Pereira
Intelligent Email: Aiding Users With Ai, Mark Dredze, Hanna Wallach, Danny Puller, Tova Brooks, Josh Carroll, Joshua Magarick, John Blitzer, Fernando Pereira
Hanna M. Wallach
Email occupies a central role in the modern workplace. This has led to a vast increase in the number of email messages that users are expected to handle daily. Furthermore, email is no longer simply a tool for asynchronous online communication---email is now used for task management, personal archiving, as well both synchronous and asynchronous online communication. This explosion can lead to ``email overload''---many users are overwhelmed by the large quantity of information in their mailboxes. In the human--computer interaction community, there has been much research on tackling email overload. Recently, similar efforts have emerged in the artificial intelligence (AI) …
Gibbs Sampling For Logistic Normal Topic Models With Graph-Based Priors, David Mimno, Hanna Wallach, Andrew Mccallum
Gibbs Sampling For Logistic Normal Topic Models With Graph-Based Priors, David Mimno, Hanna Wallach, Andrew Mccallum
Hanna M. Wallach
Previous work on probabilistic topic models has either focused on models with relatively simple conjugate priors that support Gibbs sampling or models with non-conjugate priors that typically require variational inference. Gibbs sampling is more accurate than variational inference and better supports the construction of composite models. We present a method for Gibbs sampling in non-conjugate logistic normal topic models, and demonstrate it on a new class of topic models with arbitrary graph-structured priors that reflect the complex relationships commonly found in document collections, while retaining simple, robust inference.
User Models For Email Activity Management, Mark Dredze, Hanna M. Wallach
User Models For Email Activity Management, Mark Dredze, Hanna M. Wallach
Hanna M. Wallach
A single user activity, such as planning a conference trip, typically involves multiple actions. Although these actions may involve several applications, the central point of coordination for any particular activity is usually email. Previous work on email activity management has focused on clustering emails by activity. Dredze et al. [3] accomplished this by combining supervised classifiers based on document similarity, authors and recipients, and thread information. In this paper, we take a different approach and present an unsupervised framework for email activity clustering. We use the same information sources as Dredze et al.—namely, document similarity, message recipients and authors, and …