Predicting word order universals

Exploring Word Order Universals: a Probabilistic Graphical Model Approach

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-syst...

Word Order Acquisition in Persian Speaking Children

Objectives: Persian is a pro-drop language with canonical Subject-Object-Verb (SOV) word order. This study investigates the acquisition of word order in Persian-speaking children. Methods: In the present study, participants were 60 Persian-speaking children (30 girls and 30 boys) with typically developing language skills, and aged between 30-47 months. The 30-minute language samples were audio...

Word Order

One of the primary ways in which languages differ from one another is in the order of constituents, or, as it is most commonly termed, their word order. When people refer to the word order of a language, they often are referring specifically to the order of subject, object, and verb with respect to each other, but word order refers more generally to the order of any set of elements, either at t...

Predicting Word Association Strengths

This paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (BoydGraber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh Associative Thesaurus (Kiss et al., 1973). We achieve results of r = 0.357 and ρ = 0.379, r = 0.344 and ρ = 0.300, an ρ = 0.292 and ρ = 0.363, respectively. We find Wor...

Predicting Human Word Associations