Using multiple sources of information for constraint-based morphological disambiguation

Abstract

This thesis presents a constraint-based morphological disambiguation approach that is applicable to languages with complex morphology-specifically agglutiriiitive languages with productive inflectional and derivational morphological phenomena. For morphologicciJly comiDlex languages like Turkish, automatic morphological disarnbigucition involves selecting for each token rnorphologiccil parse(s), with the right set of inflectional and derivational markers. Our system combines corpus independent hand-crafted constraint rules, constraint rules that are lecirned via unsupervised learning from a training corpus, and additioiml stcitistiCcil information obtcvined from the corpus to be morphologically disarnbigucited. The hcind-crafted rules are linguistically motivated and tuned to improve precision without sacrificing recall. In certain respects, our ai^proach has been motivated by Brill’s recent work [6], 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 reccill of 96 to 97% with a corresponding precision of 93 to 94%, and ambiguity of 1.02 to 1.03 parses per token.