Papers for Relevance Assessment by Author Soandso

Papers for Relevance Assessment by Sathyajith Kohomban

Research Question: if we can do the above, how can we learn those concepts from a generic set of labelled data, overcoming noise?

Reformulation: if we can generalize learning word senses, how can we learn those concepts from a generic set of labelled data, overcoming noise?

Paper ID (Link to PDF)	Title	Author(s)
W98-0214	Navigating maps with little or no sight: An audio-tactile approach	R. Dan Jacobson
267_pdf_2-col	Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models	Indrajit Bhattacharya, Lise Getoor and Yoshua Bengio
ciaramita	Multi-component Word Sense Disambiguation	Massimiliano Ciaramita and Mark Johnson
95_pdf_2-col	Finding Predominant Word Senses in Untagged Text	Diana McCarthy, Rob Koeling, Julie Weeds and John Carroll
W03-1022	Supersense Tagging of Unknown Nouns in WordNet	Massimiliano Ciaramita; Mark Johnson
H89-2007	Modelling Non-verbal Sounds for Speech Recognition	Wayne Ward
J02-2003	Class-Based Probability Estimation Using a Semantic Hierarchy	Stephen Clark; David Weir
C00-1028	Explaining away ambiguity: Learning verb selectional preference with Bayesian networks	Massimiliano Ciaramita; Mark Johnson
P95-1025	Statistical Sense Disambiguation with Relatively Small Corpora Using Dictionary Definitions	Alpha K. Luk
W96-0105	Selective Sampling of Effective Example Sentence Sets for Word Sense Disambiguation	Atsushi Fujii; Kentaro Inui; Takenobu Tokunaga; Hozumi Tanaka
J91-4003	The Generative Lexicon	James Pustejovsky
W98-1237	Position Paper on Appropriate Audio/Visual Turing Test	Bradley B. Custer
W98-0612	Exemplar-Based Sense Modulation	Mohsen Rais-Ghasem; Jean-Pierre Corriveau
C88-2106	Referential Properties of Generic Terms Denoting Things and Situations	Elena V. PADUCHEVA
P05-1004	Supersense Tagging of Unknown Nouns Using Semantic Similarity	James Curran