It's Only Morpho-Logical: Modeling Agreement in Cross-Linguistic Dependency Parsing

Abstract

I propose a linguistically motivated set of features to model morphological agreement and add them to MSTParser, a graph-based dependency parser (McDonald et al., 2006). Compared to the parser's built-in morphological features, the new feature set is much smaller and more accurate. Results across 21 treebanks containing varying amounts of morphological annotation demonstrate increases in accuracy of up to 5.3% absolute. Experiments are performed to investigate exactly how the features enhance performance. While some of the improvement results from the feature set capturing information unrelated to morphology, there is still significant improvement, up to 4.6% absolute, due to the agreement model. This thesis includes background on morphological agreement and dependency parsing, details on MSTParser and modifications made to it, information about the treebanks collected and the steps taken to normalize them, and descriptions of experiments and results.