Nick Rizzolo

Also published as: Nicholas Rizzolo


CogCompNLP: Your Swiss Army Knife for NLP
Daniel Khashabi | Mark Sammons | Ben Zhou | Tom Redman | Christos Christodoulopoulos | Vivek Srikumar | Nicholas Rizzolo | Lev Ratinov | Guanheng Luo | Quang Do | Chen-Tse Tsai | Subhro Roy | Stephen Mayhew | Zhili Feng | John Wieting | Xiaodong Yu | Yangqiu Song | Shashank Gupta | Shyam Upadhyay | Naveen Arivazhagan | Qiang Ning | Shaoshi Ling | Dan Roth
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


Inference Protocols for Coreference Resolution
Kai-Wei Chang | Rajhans Samdani | Alla Rozovskaya | Nick Rizzolo | Mark Sammons | Dan Roth
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task


Object Search: Supporting Structured Queries in Web Search Engines
Kim Pham | Nicholas Rizzolo | Kevin Small | Kevin Chen-Chuan Chang | Dan Roth
Proceedings of the NAACL HLT 2010 Workshop on Semantic Search

Learning Based Java for Rapid Development of NLP Systems
Nick Rizzolo | Dan Roth
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Today's natural language processing systems are growing more complex with the need to incorporate a wider range of language resources and more sophisticated statistical methods. In many cases, it is necessary to learn a component with input that includes the predictions of other learned components or to assign simultaneously the values that would be assigned by multiple components with an expressive, data dependent structure among them. As a result, the design of systems with multiple learning components is inevitably quite technically complex, and implementations of conceptually simple NLP systems can be time consuming and prone to error. Our new modeling language, Learning Based Java (LBJ), facilitates the rapid development of systems that learn and perform inference. LBJ has already been used to build state of the art NLP systems. In this paper, we first demonstrate that there exists a theoretical model that describes most NLP approaches adeptly. Second, we show how our improvements to the LBJ language enable the programmer to describe the theoretical model succinctly. Finally, we introduce the concept of data driven compilation, a translation process in which the efficiency of the generated code benefits from the data given as input to the learning algorithms.

Integer Linear Programming in NLP - Constrained Conditional Models
Ming-Wei Wang | Nicholas Rizzolo | Dan Roth
NAACL HLT 2010 Tutorial Abstracts