Papers for Relevance Assessment by Sathyajith Kohomban

Research Question: Can we generalize learning word senses by using a common set of super-senses instead of an enumerative lexicon?

Paper ID
(Link to PDF)

Title

Author(s)

C00-1028

Explaining away ambiguity: Learning verb selectional preference with Bayesian networks

Massimiliano Ciaramita; Mark Johnson

ohara

Class-based collocations for Word Sense Disambiguation

Tom O'Hara, Rebecca Bruce, Jeff Donner and Janyce Wiebe

ciaramita

Multi-component Word Sense Disambiguation

Massimiliano Ciaramita and Mark Johnson

J02-2003

Class-Based Probability Estimation Using a Semantic Hierarchy

Stephen Clark; David Weir

W98-0612

Exemplar-Based Sense Modulation

Mohsen Rais-Ghasem; Jean-Pierre Corriveau

W96-0105

Selective Sampling of Effective Example Sentence Sets for Word Sense Disambiguation

Atsushi Fujii; Kentaro Inui; Takenobu Tokunaga; Hozumi Tanaka

C90-2007

Lexical Ambiguity and The Role of Knowledge Representation in Lexicon Design

Branimir Boguraev; James Pustejovsky

C92-1032

LEFT-CORNER PARSING AND PSYCHOLOGICAL PLAUSIBILITY

Philip Resnik

P05-1004

Supersense Tagging of Unknown Nouns Using Semantic Similarity

James Curran

J91-4003

The Generative Lexicon

James Pustejovsky

W03-1022

Supersense Tagging of Unknown Nouns in WordNet

Massimiliano Ciaramita; Mark Johnson

W97-0213

A Perspective on Word Sense Disambiguation Methods and Their Evaluation

Philip Resni

267_pdf_2-col

Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models

Indrajit Bhattacharya, Lise Getoor and Yoshua Bengio

W02-0815

Improving Subcategorization Acquisition with WSD

Judita Preiss; Anna Korhonen

95_pdf_2-col

Finding Predominant Word Senses in Untagged Text

Diana McCarthy, Rob Koeling, Julie Weeds and John Carroll