Alignment Based Induction of Morphology Grammar and its Role for Bootstrapping
Damir Ćavar, Joshua Herring, Toshikazu Ikuta, Paul Rodrigues, Giancarlo Schrementi · 2004
Diff erent Alignment Based Learning (ABL) algorithms have been proposed for unsupervised grammar induction, e. g. Zaanen (2001) and Dejean (1998), in particular for the induction of syntactic rules. However, ABL seems to be better suited for the induction of morphological rules. In this paper we show how unsupervised hypothesis generation with ABL algorithms can be used to induce a lexicon and morphological rules for various types of languages, e. g. agglutinative or polysynthetic languages. The resulting morphological rules and structures are optimized with the use of confl icting constraints on the size and statistical properties of the grammars, i. e. Minimium Description Length and Minimum Relative Entropy together with Maximum Average Mutual Information. Further, we discuss how the resulting (optimal and regular) grammar can be used for lexical clustering/classifi cation for the induction of syntactic (context free) rules.