A computerized scoring system for use with content analysis scales.

Abstract 1. 1. This is a report of the development of a computerized scoring system for use with content analysis scales of speech. The goal has been to surpass the content analysis of single, isolated words in natural language and to focus the analysis on the quality and quantity of meaning carried in clauses or sentences and, eventually, in a broader contextual framework. 2. 2. Using the Gottschalk-Gleser content analysis Hostility scales, the first step taken was the use of an efficient parser that contained a sufficiently powerful grammar to characterize the subset of English that is often encountered in psychiatric interviews. Wood's Augmented Transition Network Parser was modified to run on a PDP-10 computer, and its grammar was enlarged to cover certain linguistic constructions that frequently occur in spoken discourse. In addition, a small dictionary of several hundred entries was created, permitting the maintenance of this dictionary in core. 3. 3. Since the Gottschalk-Gleser content analysis method derives a score on the basis of the action verb in a clause in conjunction with the noun-phrases that function as actors and recipients of this action, a technique was developed for assigning “meaning” to each of these constituents. Accordingly, verbs were assigned semantic features called verb-types based on the thematic categories and weights of the Gottschalk-Gleser Hostility Outward scale. 4. 4. The infinitive form of the verb in each spoken clause was retrieved from the parse and looked up in the semantic-features dictionary. The parser performed the required morphological analysis and stripped the inflectional ending of the verb, yielding its infinitive or root form. 5. 5. The computer next assigned semantic features to the noun-phrases that function as actors and recipients of the action verbs. In the simplest instances, the noun-phrase received the semantic features of its head noun. In more complex instances, for example in prepositional-phrases, noun-phrases were located as semantic markers for the initial noun-phrase. 6. 6. The scoring algorithm for arriving at the score for each clause was obtained after examining the set of semantic features assigned to the above-mentioned noun-phrases and head verb. An information structure (scoretree) was used for advice on how to interpret the combinations of noun-phrases and head verbs, and it was indexed by the verb-type. At present, each scoretree has only two levels; the first level is made up of tests for the features of the subject of the clause in question and the second level is composed of tests for the object. Which scoretree to access is determined by locating the verb-type of the head verb in the clause; the first-level node is obtained by overlapping the features of the subject noun-phrase with the features of successive first-level nodes until some partial match is achieved. The second-level node is located from the first-level node overlapping the features of the object noun-phrase with successive nodes until another partial match is achieved. Associated with the second-level node is the score that the total clause is to receive. 7. 7. In testing this automated method on 100 sentences taken at random from the manual of instructions for using the Gottschalk-Gleser content analysis scales, 60% of them were correctly parsed and scored. As another preliminary test of the information retrieved by this computerized method, six 5-min speech samples were scored by human content analysis technicians, and their scores were compared to the computerized scoring system at its present level of development. A Spearman rank difference correlation of 0.80 was obtained between the ranked scores using the two methods. 8. 8. The shortcomings and further steps to take in the development of this content analysis method are outlined and discussed.