Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Dept. of Foreign Literatures and Linguistics. / Vita. / Bibliography: leaves 194-195. / by Robert B. Rardin, II. / Ph.D.

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This study investigated the significance of semantics in computer-generated word frequency counts in response to a call for new word lists (Read, 2000; Gardner, 2007). Read claims that no corpus projects to date have produced any "definitive, stand-alone word-frequency lists" (p. 226). Many researchers are wary of the fact that the concept of a word is never clearly defined in most studies that have dealt with word frequency counts. It is clear from the research that one universally acceptable construct for the concept of word does not exist. In fact, many past word frequency counts only examine word forms without considering the word meanings and the possible effects of homography on lists. Ming-Tzu and Nation (2004) did some research on the Academic Word List (AWL) that addresses some criticisms of word-frequency lists. They evaluate the extent of homography throughout the AWL. However, words found in the AWL are often not a part of the highest frequency word-forms in English. The present study focuses on high frequency words. It evaluates a randomized sample of 46 lemmas that occur at least 1500 times in the British National Corpus (BNC). A further random sampling of 200 examples for each lemma, in context, was semantically analyzed and tallied. One hundred of these examples were from the written portion and the other 100 from the spoken portion. The list of meanings for each word was compiled using conflated WordNet senses and some additional senses. Each context was double and sometimes triple rated. The results indicate that the impact of semantic frequency versus form-based frequency is considerable. The study suggests that the presence of homography tends to be extensive in many high-frequency word forms, across major registers of the language, and within each of the four major parts of speech. It further suggests that basing frequency on semantics will considerably alter the content of a high-frequency word list.

The purpose of this study was to record a set of Mongolian monosyllabic word lists that could be used to obtain a word recognition score. A word list was developed of 190 frequently used monosyllabic words which had been selected by a group of raters. Native male and female Mongolian talkers were utilized to make digital recordings. The 190 words were divided into 10 lists of 19 words. The lists were presented to 20 normally hearing subjects at 10 different intensity levels ranging from -5 to 40 dB HL in 5 dB increments. An S-curve distribution was used to divide the words among three lists based upon the results of the word recognition testing. Word lists were assembled formulating 3 lists of 50 words and 6 half-lists of 25 words using an S-curve distribution. Logistic regression was used to calculate the psychometric functions for each list. The mean psychometric function slopes for the male and female word lists were 6.19%/dB and 5.17%/dB respectively. The 50% threshold was 14.47 dB HL for the final adjusted male and female lists.

The aim of this study was to develop, digitally record, evaluate, and psychometrically equate a set of Spanish bisyllabic word lists to be used for word recognition testing. Frequently used bisyllabic words were selected and digitally recorded by male and female Spanish talkers. Twenty normally hearing subjects were presented each word to find the percentage of words which they could correctly recognize. Each word was measured at 10 intensity levels (-5 to 40 dB HL) in increments of 5 dB. Chisquare analysis was used to determine the equivalency among the final four psychometrically equivalent word lists of 50 words, and each of the eight half-lists containing 25 words each. The results of the analysis indicated that there were no significant differences among the four-lists or eight half-lists. Only minimal adjustments (≤0.5 dB) were needed to equate the words in the lists and half-lists for the male and female talkers.

An enormous amount of the historical record is currently trapped in non-indexed handwritten format. Even after being scanned into images, only a minute fraction of the existing records can be manually transcribed / indexed with reasonable amounts of time and cost. Although progress continues to be made with automatic handwriting recognition (HR), it is not yet good enough to replace manual transcription or indexing. Much of the recent HR work has focused on incremental improvements to methods based on Hidden Markov Models (HMMs) and other similar probabilistic approaches. In this dissertation we present a fundamentally new approach to HR based on 2-D geometric warping of word images. The results of our experimentation indicate that our approach is significantly more accurate than an existing whole-word approach used for word-spotting, and may also be better than HMM-based HR approaches. Since it is a completely new method, we also believe there is potential for improvement and future work that builds on this approach. In addition, we demonstrate that the approach can be used effectively in the related application domain of signature verification and forgery detection.

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