A Formal Mechanism to Process Natural Language: Grammatical Number Recognition

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1998
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Swarthmore College. Dept. of Linguistics
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Thesis (B.A.)
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Full copyright to this work is retained by the student author. It may only be used for non-commercial, research, and educational purposes. All other uses are restricted.
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Abstract
The purpose of this research is to attempt to create a grammar or mechanism that will be able to generate or comprehend grammatical number strings in Standard American English while either not generating or not accepting ungrammatical number strings. Clearly, the human brain is capable of performing this task of weeding out any ungrammatical strings and comprehending and generating legal strings. For example, when a native speaker of English is presented with the string "one hundred forty four thousand three hundred twelve" it is conceivable that the speaker has never encountered this number before, never spoken it, never heard it, and never read it. However, the hearer readily recognizes this string as a legal number, a possible combination of components in the English language. At the same time, the speaker will never generate or accept as grammatical a string such as "forty hundred three thousand one thousand." Every individual word in that string is a word of English, and a number term, but even though the speaker has never been taught that that specific string is not a number, the speaker intuitively recognizes it as nonsense. The questions that I seek to answer are: is it possible to mimic the rules in the human processing of these strings that allow only legal strings to be recognized as legitimate numbers, and what mechanisms are most efficient and effective in simulating human number comprehension. In this research, we shall be concerned entirely with number strings used to describe real quantities, the numbers we use to count things. We will not be concerned with numbers such as "nineteen ninety seven" (as in the year 1997), nor will we be concerned with numbers that do not correspond to real quantities, such as "a million billion trillion." The idea for this project stems from work I undertook at the Institute for Research in Cognitive Science at the University of Pennsylvania, in the summer of 1997. The models used will be pencil and paper models, not actual working devices.
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