Documents often have inherently parallel structure: they may consist of a text and ries, or an abstract and a body, or parts presenting alternative views on the same problem. Reve...
Public health-related topics are difficult to identify in large conversational datasets like Twitter. This study examines how to model and discover public health topics and themes ...
Kyle W. Prier, Matthew S. Smith, Christophe G. Gir...
This paper presents a method for learning a semantic parser from ambiguous supervision. Training data consists of natural language sentences annotated with multiple potential mean...
We explore the utility of different types of topic models for retrieval purposes. Based on prior work, we describe several ways that topic models can be integrated into the retrie...
For many supervised learning problems, we possess prior knowledge about which features yield similar information about the target variable. In predicting the topic of a document, ...
Ted Sandler, John Blitzer, Partha Pratim Talukdar,...