Papers for Relevance Assessment by Author Soandso

Papers for Relevance Assessment by Noah A. Smith

Research Question: How can unsupervised learning (i.e., learning from unlabeled data) be carried out efficiently and in a Bayesian framework for log-linear (random field) models of sequence data?

Paper ID (Link to PDF)	Title	Author(s)
H01-1052	Mitigating the Paucity-of-Data Problem: Exploring the Effect of Training Corpus Size on Classifier Performance for Natural Language Processing	M. Banko; E. Brill
W99-0613	Unsupervised Models for Named Entity Classification	Michael Collins; Yoram Singer
niu1	Context clustering for Word Sense Disambiguation based on modeling pairwise context similarities	Cheng Niu, Wei Li, Rohini K. Srihari, Huifeng Li and L. Crist
W05-0612	An Expectation Maximization Approach to Pronoun Resolution	Colin Cherry; Shane Bergsma
J98-1002	Similarity-based Word Sense Disambiguation	Yael Karov; Shimon Edelman
C92-2070	Word-Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora	David Yarowsky
W96-0104	Learning Similarity-based Word Sense Disambiguation from Sparse Data	Yael Karov; Shimon Edelman
N01-1023	Applying Co-Training Methods to Statistical Parsing	Anoop Sarkar
W00-0701	Learning in Natural Language: Theory and Algorithmic Approaches	Dan Roth
W03-0417	Training a Naive Bayes Classifier via the EM Algorithm with a Class Distribution Constraint	Yoshimasa Tsuruoka; Jun'ichi Tsujii
P02-1044	Word Translation Disambiguation Using Bilingual Bootstrapping	Cong Li; Hang Li
P01-1005	Scaling to Very Very Large Corpora for Natural Language Disambiguation	Michele Banko; Eric Brill
P00-1069	Word Sense Disambiguation by Learning from Unlabeled Data	Seong-Bae Park; Byoung-Tak Zhang; Yung Taek Kim
W96-0203	Unsupervised Learning of Syntactic Knowledge: Methods and Measures	R. Basili; A. Marziali; M.T. Pazienza; P. Velardi
H92-1046	Lexical Disambiguation using Simulated Annealing	Jim Cowie; Joe Guthrie; Louise Guthrie