<OT> New Posting: ROA-964

[roa at ruccs.rutgers.edu]

ROA 964-0408

Discovering Underlying Forms: Contrast Pairs and Ranking

Nazarre Merchant <nazarre at gmail.com>

Direct link: http://roa.rutgers.edu/view.php3?roa=964


Abstract:
Phonological learners must acquire a lexicon of underlying
forms and a constraint ranking. These must be acquired simultaneo
usly, 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.

Comments: 
Keywords: Error-driven learning, BCD, RCD, Ranking, ERC
Areas: Phonology,Learnability,Computation,Formal Analysis
Type: PhD Dissertation

Direct link: http://roa.rutgers.edu/view.php3?roa=964