Utilizing Linguistic Context To Improve Individual and Cohort Identification in Typed Text

The process of producing written text is complex and constrained by pressures that range from physical to psychological. In a series of three sets of experiments, this thesis demonstrates the effects of linguistic context on the timing patterns of the production of keystrokes. We elucidate the effect of linguistic context at three different levels of granularity: The first set of experiments illustrate how the nontraditional syntax of a single linguistic construct, the multi-word expression, can create significant changes in keystroke production patterns. This set of experiments is followed by a set of experiments that test the hypothesis on the entire linguistic output of an individual. By taking into account linguistic context, we are able to create more informative feature-sets, and utilize these to improve the accuracy of keystroke dynamic-based user authentication. Finally, we extend our findings to entire populations, or demographic cohorts. We show that typing patterns can be used to predict a group\u27s gender, native language and dominant hand. In addition, keystroke patterns can shed light on the cognitive complexity of a task that a typist is engaged in. The findings of these experiments have far-reaching implications for linguists, cognitive scientists, computer security researchers and social scientists

Annotation of Children's Oral Narrations: Modeling Emergent Narrative Skills for Computational Applications

We present an annotation method for developing a model of children’s comprehension that differentiates between their recall for the objective content of a story and inferred content. We apply the annotation method to a corpus of retellings, in which children retell the same story on three successive days. Our results indicate differences over time: on Day three, children have a more evenly distributed recall of events through- out the story, and include significantly more inferences. The results suggest a cognitive bootstrapping effect. We discuss the potential for application to diagnostic assessment of children’s narrative skills and tutorial applications

Applying the Pyramid Method in the 2006 Document Understanding Conference

The pyramid evaluation effort for the 2006 Document Understanding Conference involved twenty-two sites on twenty document sets. Each pyramid content model (one per document set) was constructed from four human summaries. Peer systems were scored using the modified pyramid score introduced in DUC 2005. ANOVAs with score as the independent variable and nine factors yielded three significant factors: document set, peer, and content responsiveness. There were many more significant differences among peer systems in 2006 than for DUC 2005. We speculate this is due to a combination of improved systems and improvements in our evaluation procedures

Detecting Language Impairments in Autism: A Computational Analysis of Semi-structured Conversations with Vector Semantics

Many of the most significant impairments faced by individuals with autism spectrum disorder (ASD) relate to pragmatic (i.e. social) language. There is also evidence that pragmatic language differences may map to ASD-related genes. Therefore, quantifying the social-linguistic features of ASD has the potential to both improve clinical treatment and help identify gene-behavior relationships in ASD. Here, we apply vector semantics to transcripts of semi-structured interactions with children with both idiopathic and syndromic ASD. We find that children with ASD are less semantically similar to a gold standard derived from typically developing participants, and are more semantically variable. We show that this semantic similarity measure is affected by transcript word length, but that these group differences persist after removing length differences via subsampling. These findings suggest that linguistic signatures of ASD pervade child speech broadly, and can be automatically detected even in less structured interactions