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Discovering underlying forms: contrast pairs and ranking
Descriptive
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Title
Discovering underlying forms: contrast pairs and ranking
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Merchant
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Nazarre Nathaniel
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Nazarre Nathaniel Merchant
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author
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Tesar
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Bruce
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Advisory Committee
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Bruce Tesar
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chair
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Prince
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Alan
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Advisory Committee
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Alan Prince
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internal member
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de Lacy
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Paul
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Advisory Committee
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Paul de Lacy
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Pater
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Joe
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Advisory Committee
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Joe Pater
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outside member
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Rutgers University
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Graduate School - New Brunswick
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theses
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2008
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2008-05
Language
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English
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electronic
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application/pdf
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text/xml
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xi, 188 pages
Abstract
Phonological learners must acquire a lexicon of underlying forms and a constraint ranking. These must be acquired simultaneously, as the ranking and the underlying forms are interdependent. Exhaustive search of all possible lexica is intractable; the space of lexica is simply too large. Searching the underlying forms for each overt form in isolation poses other problems. A single overt form is often highly ambiguous among both underlying forms and rankings. In this dissertation I propose a learning algorithm that attends to pairs of overt forms that differ in exactly one morpheme. These pairs can exhibit less ambiguity than the isolated overt forms, while still providing a reduced search space.
The algorithm first assigns underlying values to occurrences of features whose surface realization never alternates; the other underlying features are left initially unset (Tesar et al., 2003). Pairs of overt forms that differ in one morpheme are then constructed. The algorithm then considers the possible values of unset features for each pair, processing pairs with the fewest unset features first. It uses inconsistency detection (Tesar, 1997) to test sets of values of unset features for viability. A set of values for the unset features is viable if it produces the correct overt forms under some ranking. Those feature values which are common across all viable solutions are then set. In the process of testing for inconsistency for each set of values of unset features a set of winner-loser pairs is generated. The learner determines the ranking restrictions jointly entailed by these sets of winner-loser pairs. These ranking restrictions are then maintained while processing all further contrast pairs. After all pairs have been processed, any still unset feature values are assigned default values. The general success of the algorithm depends upon these features being fully predictable in the output. A ranking is then obtained from this lexicon using Biased Constraint Demotion (Prince and Tesar, 2004).
Fixing all non-alternating features reduces the effective lexical search space. The algorithm further reduces the lexical search space by breaking up the search into tractable local pair searches. Extracting shared ranking information from winner-loser pairs generated from inconsistency detection restricts which featural combinations for future contrast pairs will be viable providing information that is otherwise unavailable to the learner.
The algorithm first assigns underlying values to occurrences of features whose surface realization never alternates; the other underlying features are left initially unset (Tesar et al., 2003). Pairs of overt forms that differ in one morpheme are then constructed. The algorithm then considers the possible values of unset features for each pair, processing pairs with the fewest unset features first. It uses inconsistency detection (Tesar, 1997) to test sets of values of unset features for viability. A set of values for the unset features is viable if it produces the correct overt forms under some ranking. Those feature values which are common across all viable solutions are then set. In the process of testing for inconsistency for each set of values of unset features a set of winner-loser pairs is generated. The learner determines the ranking restrictions jointly entailed by these sets of winner-loser pairs. These ranking restrictions are then maintained while processing all further contrast pairs. After all pairs have been processed, any still unset feature values are assigned default values. The general success of the algorithm depends upon these features being fully predictable in the output. A ranking is then obtained from this lexicon using Biased Constraint Demotion (Prince and Tesar, 2004).
Fixing all non-alternating features reduces the effective lexical search space. The algorithm further reduces the lexical search space by breaking up the search into tractable local pair searches. Extracting shared ranking information from winner-loser pairs generated from inconsistency detection restricts which featural combinations for future contrast pairs will be viable providing information that is otherwise unavailable to the learner.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 185-187).
Subject
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Topic
Linguistics
Subject
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Topic
Grammar, Comparative and general--Syntax
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17354
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ETD_951
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doi:10.7282/T3513ZHF
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ETD doctoral
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The author owns the copyright to this work.
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Copyright protected
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Open
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Nazarre Merchant
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Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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