This study describes a method for developing an empirically based, computer derived classification system. 618 psychological abstracts were coded in machine language for computer processing. The total text consisted of approximately 50,000 words of which nearly 6,800 were unique words. The computer program arranged these words in order of frequency of occurrence. From the list of words which occurred 20 or more times, excluding syntactical terms, such as, and, but, of, etc., the investigator selected 90 words for use as index terms. These were arranged in a data matrix with the terms on the horizontal and the document number on the vertical axis. The cells contained the number of times the term was used in the document. Based on these data, a correlation matrix, 90x90 in size, was computed which showed the relationship of each term to every other term. The matrix was factor analyzed and the first 10 eigenvectors were selected as factors. These were rotated for meaning and interpreted as major categories in a classification system. These factors were compared with, and shown to be compatible but not identical to, the classification system used by the American Psychological Association. The results demonstrate the feasibility of an empirically derived classification system and establish the value of factor analysis as a technique in language data processing.
{"title":"The construction of an empirically based mathematically derived classification system","authors":"H. Borko","doi":"10.1145/1460833.1460865","DOIUrl":"https://doi.org/10.1145/1460833.1460865","url":null,"abstract":"This study describes a method for developing an empirically based, computer derived classification system. 618 psychological abstracts were coded in machine language for computer processing. The total text consisted of approximately 50,000 words of which nearly 6,800 were unique words. The computer program arranged these words in order of frequency of occurrence. From the list of words which occurred 20 or more times, excluding syntactical terms, such as, and, but, of, etc., the investigator selected 90 words for use as index terms. These were arranged in a data matrix with the terms on the horizontal and the document number on the vertical axis. The cells contained the number of times the term was used in the document. Based on these data, a correlation matrix, 90x90 in size, was computed which showed the relationship of each term to every other term. The matrix was factor analyzed and the first 10 eigenvectors were selected as factors. These were rotated for meaning and interpreted as major categories in a classification system. These factors were compared with, and shown to be compatible but not identical to, the classification system used by the American Psychological Association. The results demonstrate the feasibility of an empirically derived classification system and establish the value of factor analysis as a technique in language data processing.","PeriodicalId":307707,"journal":{"name":"AIEE-IRE '62 (Spring)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1899-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115632728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automatic computers, both analog and digital, have attained a prominent place in most modern engineering curricula. The computer is recognized as an important engineering design tool permitting the student to test the efficacy of a large number of design hypotheses to determine an optimum design. In these applications the computer is favored because it enables the student to dispense with laborious hand calculations and because it familiarizes the engineering student with techniques which will subsequently be valuable to him in industry. The application of automatic computers to another category of engineering courses - courses in methods of analysis - is more controversial.
{"title":"The use of computers in analysis","authors":"W. Karplus, L. D. Kovach","doi":"10.1145/1460833.1460860","DOIUrl":"https://doi.org/10.1145/1460833.1460860","url":null,"abstract":"Automatic computers, both analog and digital, have attained a prominent place in most modern engineering curricula. The computer is recognized as an important engineering design tool permitting the student to test the efficacy of a large number of design hypotheses to determine an optimum design. In these applications the computer is favored because it enables the student to dispense with laborious hand calculations and because it familiarizes the engineering student with techniques which will subsequently be valuable to him in industry. The application of automatic computers to another category of engineering courses - courses in methods of analysis - is more controversial.","PeriodicalId":307707,"journal":{"name":"AIEE-IRE '62 (Spring)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1899-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122779915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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