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An experimental analogue of the initial interview is used to investigate the effects of interviewer ambiguity-specificity and topical focus, i.e., a low- vs. a high-anxiety arousing topic, on interviewee vocabulary diversity, measured by the type-token ratio. This paper is one of a series dealing with the effects of both the ambiguity level and the anxiety arousing potential in an interviewer's remarks on interviewee's responses. In the present study authors are concerned with the effects of the above two interviewer variables on interviewee vocabulary diversity, as measured by the types token-ratio, i.e., the ratio of different words (types) to the total number of words (tokens).

The article sets out a mechanical procedure for eliminating lexical ambiguities in machine translation in cases where a word in the source language has a number of equivalents in the target language. It can be argued for this method that it does away with the need for multiple meaning analyses, for "look-up" operations, and for some machine subroutines and programming, and thus minimizes machine time. Furthermore, it is generally agreed that machine translation will be realized for one limited universe at a time and not for "language" as a whole.