Learning Morphological Disambiguation Rules for Turkish

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 328–334, New York, June 2006. c⃝2006 Association for Computational Linguistics Learning Morphological Disambiguation Rules for Turkish Deniz Yuret Dept. of Computer Engineering Koc¸ University ˙Istanbul, Turkey dyuret@ku.edu.tr Ferhan T¨ure Dept. of Computer Engineering Koc¸ University ˙Istanbul, Turkey fture@ku.edu.tr Abstract In this paper, we present a rule based model for morphological disambiguation of Turkish. The rules are generated by a novel decision list learning algorithm us- ing 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 morpho- logically ambiguous. Furthermore, it is possible for a word to take an unlimited number of suffixes, therefore the number of possible morphological tags is unlim- ited. 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 compari- son, when we train a single decision list on full tags instead of using separate models on each feature we get 91% accuracy. 1 Introduction Morphological disambiguation is the task of select- ing the correct morphological parse for a given word in a given context. The possible parses of a word are generated by a morphological analyzer. In Turk- ish, close to half the words in running text are mor- phologically ambiguous. Below is a typical word “masalı” with three possible parses. masal+Noun+A3sg+Pnon+Acc (= the story) masal+Noun+A3sg+P3sg+Nom (= his story) masa+Noun+A3sg+Pnon+NomˆDB+Adj+With (= with tables) Table 1: Three parses of the word “masalı” The first two parses start with the same root, masal (= story, fable), but the interpretation of the following +ı suffix is the Accusative marker in one case, and third person possessive agreement in the other. The third parse starts with a different root, masa (= table) followed by a derivational suffix +lı (= with) which turns the noun into an adjective. The symbol ˆDB represents a derivational boundary and splits the parse into chunks called inflectional groups (IGs).1 We will use the term feature to refer to individual morphological features like +Acc and +With; the term IG to refer to groups of features split by deriva- tional boundaries (ˆDB), and the term tag to refer to the sequence of IGs following the root. Morphological disambiguation is a useful first step for higher level analysis of any language but it is especially critical for agglutinative languages like Turkish, Czech, Hungarian, and Finnish. These lan- guages have a relatively free constituent order, and 1See (Oflazer et al., 1999) for a detailed description of the morphological features used in this paper. 328 syntactic relations are partly determined by morpho- …

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