Exploring Word Order Universals: a Probabilistic Graphical Model Approach
Xia Lu · 2013
In this paper we propose a probabilistic graphical model as an innovative framework for studying typological universals. We view language as a system and linguistic features as its components whose relationships are encoded in a Directed Acyclic Graph (DAG). Taking discovery of the word order universals as a knowledge discovery task we learn the graphical representation of a word order sub-system which reveals a finer structure such as direct and indirect dependencies among word order features. Then probabilistic inference enables us to see the strength of such relationships: given the observed value of one feature (or combination of features), the probabilities of values of other features can be calculated. Our model is not restricted to using only two values of a feature. Using imputation technique and EM algorithm it can handle missing values well. Model averaging technique solves the problem of limited data. In addition the incremental and divide-and-conquer method addresses the areal and genetic effects simultaneously instead of separately as in Daumé III and Campbell (2007). 1