LINGUIST List 13.939

Fri Apr 5 2002

Diss: Psycholing, Computational Ling:Roland "Verb..."

Editor for this issue: Karolina Owczarzak <>


  1. douglas.roland, Psycholing, Computational Ling: Roland "Verb Sense and..."

Message 1: Psycholing, Computational Ling: Roland "Verb Sense and..."

Date: Thu, 04 Apr 2002 18:59:33 +0000
From: douglas.roland <>
Subject: Psycholing, Computational Ling: Roland "Verb Sense and..."

New Dissertation Abstract

Institution: University of Colorado at Boulder
Program: Graduate English Department
Dissertation Status: Completed
Degree Date: 2001

Author: Douglas Roland 

Dissertation Title: 
Verb Sense and Verb Subcategorization Probabilities

Dissertation URL:

Linguistic Field: 
Text/Corpus Linguistics, Psycholinguistics, Computational Linguistics

Dissertation Director 1: Daniel Jurafsky
Dissertation Director 2: Lise Menn

Dissertation Abstract: 

This dissertation investigates a variety of problems in
psycholinguistics and computational linguistics caused by the
differences in verb subcategorization probabilities found between
various corpora and experimental data sets. For psycholinguistics,
these problems include the practical problem of which frequencies to
use for norming psychological experiments, as well as the more
theoretical issue of which frequencies are represented in the mental
lexicon and how those frequencies are learned. In computational
linguistics, these problems include the decreases in the accuracy of
probabilistic applications such as parsers when they are used on
corpora other than the one on which they were trained.

Evidence is presented showing that different senses of verbs and their
corresponding differences in subcategorization, as well as inherent
differences between the production of sentences in psychological
norming protocols and language use in context, are important causes of
the subcategorization frequency differences found between corpora.
This suggests that verb subcategorization probabilities should be
based on individual senses of verbs rather than the whole verb lexeme,
and that 'test tube' sentences are not the same as 'wild' sentences.
Hence, the influences of experimental design on verb subcategorization
probabilities should be given careful consideration.

This dissertation will demonstrate a model of how the relationship
between verb sense and verb subcategorization can be employed to
predict verb subcategorization based on the semantic context preceding
the verb in corpus data. The predictions made by the model are shown
to be the same as predictions made by human subjects given the same
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