Department of Computer Engineering2024-11-09200610.3115/1220835.12208772-s2.0-84858435058https://aclanthology.org/N06-1042/https://hdl.handle.net/20.500.14288/14038In this paper, we present a rule based model for morphological disambiguation of Turkish. The rules are generated by a novel decision list learning algorithm using supervised training. Morphological ambiguity (e.g. lives = live+s or life+s) is a challenging problem for agglutinative languages like Turkish where close to half of the words in running text are morphologically ambiguous. Furthermore, it is possible for a word to take an unlimited number of suffixes, therefore the number of possible morphological tags is unlimited. We attempted to cope with these problems by training a separate model for each of the 126 morphological features recognized by the morphological analyzer. The resulting decision lists independently vote on each of the potential parses of a word and the final parse is selected based on our confidence on these votes. The accuracy of our model (96%) is slightly above the best previously reported results which use statistical models. For comparison, when we train a single decision list on full tags instead of using separate models on each feature we get 91% accuracy.Computer engineeringConference proceedinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84858435058&doi=10.3115%2f1220835.1220877&partnerID=40&md5=39587bf525c9097f0d248365b4c392d3N/A8618