Natural Language Processing and Translation using Augmented Transition Networks and Semantic Networks

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2003
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Haverford College. Department of Computer Science
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eng
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Haverford users only
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Abstract
The problem of computers understanding and communicating with humans using natural languages such as English is a complicated task with many details to examine and explore. The goal of this project, then, is to examine some of the established data structures and methods used to enable computers to understand and generate natural language. In an attempt to contribute some original material, we will also consider how a computer might be able to translate sentences between English and Spanish. The techniques covered in this paper are well-established data structures and methods for parsing and generating natural language sentences. In particular, we will pay close attention to the augmented transition network model (ATN) and semantic networks. The ATN data structure is a powerful mechanism for interpreting natural language constructs, most notably due to its ability to both parse and generate language with a single network. Extending the ATN structure slightly will also allow for our goal of language translation. The semantic network model will assist in this endeavor by representing the input data as a network of entity nodes connected by labeled arcs that represent the relationship between nodes. This model abstracts the input into a form independent from the source and target languages, facilitating the task of translation immensely. Finally, we will provide a demonstration of how SNePS, a LISP-based system that incorporates ATNs and semantic networks, translates a simple set of sentences using the techniques described.
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