  Automatic text tagging is an important component in higher level analysis of
text corpora, and its output can be used in many natural language processing
applications. In languages like Turkish or Finnish, with agglutinative
morphology, morphological disambiguation is a very crucial process in tagging,
as the structures of many lexical forms are morphologically ambiguous. This
paper describes a POS tagger for Turkish text based on a full-scale two-level
specification of Turkish morphology that is based on a lexicon of about 24,000
root words. This is augmented with a multi-word and idiomatic construct
recognizer, and most importantly morphological disambiguator based on local
neighborhood constraints, heuristics and limited amount of statistical
information. The tagger also has functionality for statistics compilation and
fine tuning of the morphological analyzer, such as logging erroneous
morphological parses, commonly used roots, etc. Preliminary results indicate
that the tagger can tag about 98-99\% of the texts accurately with very minimal
user intervention. Furthermore for sentences morphologically disambiguated with
the tagger, an LFG parser developed for Turkish, generates, on the average,
50\% less ambiguous parses and parses almost 2.5 times faster. The tagging
functionality is not specific to Turkish, and can be applied to any language
with a proper morphological analysis interface.
