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Title:Minimal supervision for language learning: bootstrapping global patterns from local knowledge
Author(s):Connor, Michael
Advisor(s):Roth, Dan
Contributor(s):Fisher, Cynthia L.; Stevenson, Suzanne; DeJong, Gerald F.; Hockenmaier, Julia C.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Doctoral
Subject(s):Machine Learning
Natural Language Processing
Language Acquisition
Psycholinguistics
Abstract:A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate arguments, and assign semantic roles to those constituents. Each step depends on prior lexical and syntactic knowledge. Where do children begin in solving this problem when learning their first languages? To experiment with different representations that children may use to begin understanding language, we have built a computational model for this early point in language acquisition. This system, BabySRL, learns from transcriptions of natural child-directed speech and makes use of psycholinguistically plausible background knowledge and realistically noisy semantic feedback to begin to classify sentences at the level of ``who does what to whom.'' Starting with simple, psycholinguistically-motivated representations of sentence structure, the BabySRL is able to learn from full semantic feedback, as well as a supervision signal derived from partial semantic background knowledge. In addition we combine the BabySRL with an unsupervised Hidden Markov Model part-of-speech tagger, linking clusters with syntactic categories using background noun knowledge so that they can be used to parse input for the SRL system. The results show that proposed shallow representations of sentence structure are robust to reductions in parsing accuracy, and that the contribution of alternative representations of sentence structure to successful semantic role labeling varies with the integrity of the parsing and argument-identification stages. Finally, we enable the BabySRL to improve both an intermediate syntactic representation and its final semantic role classification. Using this system we show that it is possible for a simple learner in a plausible (noisy) setup to begin comprehending simple semantics when initialized with a small amount of concrete noun knowledge and some simple syntax-semantics mapping biases, before acquiring any specific verb knowledge.
Issue Date:2012-02-06
Genre:thesis
URI:http://hdl.handle.net/2142/29824
Rights Information:Copyright 2011 Michael James Connor
Date Available in IDEALS:2012-02-06
Date Deposited:2011-12


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