Open-ended comprehension questions are a common type of assessment used to evaluate how well students understand one of multiple documents. Our aim is to use natural language processing (NLP) to infer the level and type of inferencing within readers' answers to comprehension questions using linguistic and semantic features within their responses.…

This dissertation investigates the temporal relation identification task. The goal is to construct consistent temporal relations between temporal entities (e.g., events and time expressions) in a narrative. Constructing consistent temporal relations is challenging due to the exponential increase in the number of candidates for temporal relations…

The fundamental problem faced by automatic text understanding in Natural Language Processing (NLP) is to identify semantically related pieces of text and integrate them together to compute the meaning of the whole text. However, the principle of compositionality runs into trouble very quickly when real language is examined with its frequent…

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…

Topic models like latent Dirichlet allocation (LDA) provide a framework for analyzing large datasets where observations are collected into groups. Although topic modeling has been fruitfully applied to problems social science, biology, and computer vision, it has been most widely used to model datasets where documents are modeled as exchangeable…

Purpose: The purpose of this paper is to present a computational approach and a toolset to infer spatial displacements as they occur in route narrative documents and report on first experiments done to produce computer-aided learning (CAL) applications and instructional design editors that exploit the inferred georeferenced itineraries.…