  This paper presents a constraint-based morphological disambiguation approach
that is applicable languages with complex morphology--specifically
agglutinative languages with productive inflectional and derivational
morphological phenomena. 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 system
combines corpus independent hand-crafted constraint rules, constraint rules
that are learned via unsupervised learning from a training corpus, and
additional statistical information from the corpus to be morphologically
disambiguated. The hand-crafted rules are linguistically motivated and tuned to
improve precision without sacrificing recall. The unsupervised learning process
produces two sets of rules: (i) choose rules which choose morphological parses
of a lexical item satisfying constraint effectively discarding other parses,
and (ii) delete rules, which delete parses satisfying a constraint. 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 percent 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 percent with a corresponding precision of 93 to 94 percent,
and ambiguity of 1.02 to 1.03 parses per token.
