6 papers
The Use of Classifiers in Sequential Inference
Vasin Punyakanok, Dan Roth
We study the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints. In particular, we devel…
A Sequential Model for Multi-Class Classification
Yair Even-Zohar, Dan Roth
Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general purpose learning methods and are usu…
A Classification Approach to Word Prediction
Yair Even-Zohar, Dan Roth
The eventual goal of a language model is to accurately predict the value of a missing word given its context. We present an approach to word prediction that is based on learning a…
A Learning Approach to Shallow Parsing
Marcia Muñoz, Vasin Punyakanok, Dan Roth +1
A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors t…
Applying System Combination to Base Noun Phrase Identification
Erik F. Tjong Kim Sang, Walter Daelemans, Herve Dejean +4
We use seven machine learning algorithms for one task: identifying base noun phrases. The results have been processed by different system combination methods and all of these outpe…
Learning to Resolve Natural Language Ambiguities: A Unified Approach
Dan Roth
We analyze a few of the commonly used statistics based and machine learning algorithms for natural language disambiguation tasks and observe that they can be re-cast as learning li…