  This thesis presents a constraint-based morphological disambiguation approach
that is applicable to languages with complex morphology--specifically
agglutinative languages with productive inflectional and derivational
morphological phenomena. For morphologically complex languages like Turkish,
automatic morphological disambiguation involves selecting for each token
morphological parse(s), with the right set of inflectional and derivational
markers. Our system combines corpus independent hand-crafted constraint rules,
constraint rules that are learned via unsupervised learning from a training
corpus, and additional statistical information obtained from the corpus to be
morphologically disambiguated. The hand-crafted rules are linguistically
motivated and tuned to improve precision without sacrificing recall. In certain
respects, our approach has been motivated by Brill's recent work, but with the
observation that his transformational approach is not directly applicable to
languages like Turkish. Our approach also uses a novel approach to unknown word
processing by employing a secondary morphological processor which recovers any
relevant inflectional and derivational information from a lexical item whose
root is unknown. With this approach, well below 1% of the tokens remains as
unknown in the texts we have experimented with. Our results indicate that by
combining these hand-crafted, statistical and learned information sources, we
can attain a recall of 96 to 97% with a corresponding precision of 93 to 94%,
and ambiguity of 1.02 to 1.03 parses per token.
