Natural language is highly complex and can be challenging for some learners, yet the contribution of complexity to individual differences in language learning remains poorly understood. This poor understanding appears due to both a lack of consensus among researchers regarding what complexity is, and to on-line language research often employing…

With the ever increasing number of studies on formulaic language, we are beginning to learn more about the processing of formulaic language (e.g. Ellis et al. 2008; Siyanova et al. 2011), its use in speech (e.g. Aijmer 1996; Wood 2012) and writing (e.g. Hyland 2008a, 2008b) and its application in natural language processing (e.g. Tschichold 2000).…

Much recent work in natural language processing treats linguistic analysis as an inference problem over graphs. This development opens up useful connections between machine learning, graph theory, and linguistics. The first part of this dissertation formulates syntactic dependency parsing as a dynamic Markov random field with the novel…