if one could choose a course that would result to be the ideal learning experience to that individual, what would it be? one might say an Anatomy or Physiology.

This dissertation presents a new approach to solving the coreference resolution problem for a natural language processing (NLP) task known as information extraction. It describes a new system, named resolve, that uses machine learning techniques to determine when two phrases in a text co-refer, i.e., refer to the same thing. Resolve can be used as a component within an information extraction system -- a system that extracts information automatically from a corpus of texts that all focus on the same topic area -- or it can be used as a stand-alone system to evaluate the relative contribution of different types of knowledge to the coreference resolution process. Resolve represents an improvement over previous approaches to the coreference resolution problem, in that it uses a machine learning algorithm to handle some of the work that had previously been performed manually by a know...

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This proposal paper is being presented in partial fulfillment of the Ph.D. requirements of the Department of Computer and Information Sciences at the University of Delaware. In this paper, I discuss a user modeling architecture for ICICLE, a natural language system intended for use as a writing tutor for deaf learners of written English. This proposed design, intended to model dynamic aspects of a learner over the passage of time, the acquisition of new knowledge, and multiple sessions with the system, includes components to track the history of interaction with a given user as well as a very complex, dynamic model of user interlanguage grammar and domain knowledge. It has been based on research in language acquisition and in the acquisition of cognitive skills. The focus of the work described in this proposal is the development of the model of interlanguage status, which will be used in the analysis of user language production and in the generation of user-tailored explanations. Conte...

Abstract. The work described here pertains to ICICLE,an intelligent tutoring system for which we have designed a user model to supply data for intelligent natural language parse disambiguation. This model attempts to capture the user’s mastery of various grammatical units and thus can be used to predict the grammar rules he or she is most likely using when producing language. Because ICICLE’s user modeling component must infer the user’s language mastery on the basis of limited writing samples,it makes use of an inferencing mechanism that will require knowledge of stereotypic acquisition sequences in the user population. We discuss in this paper the methodology of how we have applied an empirical investigation into user performance in order to derive the sequence of stereotypes that forms the basis of our modeling component’s reasoning capabilities. Key words. CALL,empirical analysis,ITS,NLP,parse disambiguation,student modeling, stereotypes 1.

People with severe speech and motor impairments (SSMI) sometimes use augmentative communication devices to help them communicate. While these devices can provide speech synthesis or text output, the rate of communication is typically very slow. Consequently, augmentative communication users often develop telegraphic patterns of language usage. A natural language processing technique termed compansion (COMPression-expANSION) has been developed that expands uninflected content words (i.e., compressed or telegraphic utterances) into syntactically and semantically well-formed sentences. While originally designed as a rate enhancement technique, compansion may also be viewed as a potential tool to support English literacy for augmentative communication users. Accurate grammatical feedback from ill-formed inputs would be very beneficial in the learning process. However, the problems of dealing with inherently ambiguous errors and multiple corrections are not trivial. This paper proposes the addition of an adaptive user language model as a way to address some of these difficulties. It also discusses a possible implementation strategy using grammatical mal-rules for IPG (Intelligent Parser/Generator), a prototype system that uses the compansion technique.

In this paper we discuss an envisioned text planner for a computer-assisted instruction tool for deaf learners of English. We describe the problem of deaf literacy and overview our system, designed to act as a writing tutor that generates text to explain the errors found in a user's written compositions. These explanations are created according to a response anatomy that divides the planning task into a bottom-up phase for grouping and ordering explanations and a top-down phase for building a hierarchical text plan to create the explanatory text. This is then followed by a revision of the complete plan to place it in its greater context. At all levels of planning, the explanation is personalized to the individual learner's styles and knowledge in order to maximize the learning experience. Introduction The problem of deaf literacy has been well-documented and has far reaching effects on every aspect of deaf students ' education. Although data on writing skills is difficult to obtain, ...

In this paper we discuss how generation issues affect the design of a computer-assisted language leaming tool designed to teach written English as a second language to deaf users of American Sign Language. We discuss a dual-component linguistic model that attempts to reflect the generation process of the leamers. The first model component captures the influence of the first language on the acquisition of the second. The second model component captures the process of second language acquisition itself. The linguistic

Several researchers have noted the local coherence exhibited by discourse (Sidner 1979; Grosz, Joshi, and Weinstein 1983; Carter 1987; etc.). A primary component of this local coherence is the way the local focus of the discourse shifts from one sentence to the next and the way this shifting is marked by linguistic choices made by the writer/speaker.