The nature of affixing in written English

Any algorithmic study of written English must sooner or later face the problem of unscrambling English affixes. The role of affixes is crucial in the study of word-breaking practice. In the automatic determination of the parts of speech (a central feature of automatic syntactic analysis), the suppressing action of affixes must be understood in detail. In the determination of English citation forms, complete lists of affixes are necessary. The inflection of English verbs is tied up with the existence of suffixes. Existing definitions of affixes suffer because they are neither computable nor in general agreement with one another, and none of them refers directly to written English. Existing lists of affixes vary widely in size and content, implying a lack of agreement as to what constitutes a complete listing of English affixes, or how one is to be obtained. In this paper we show that there is a natural structural definition of English affixes, and that this definition can be implemented on existing word lists to provide exhaustive affix lists. In particular, the definition is applied to all the two-vowel string words in the Shorter Oxford Dictionary , and a complete list of the resulting affixes is provided. Some applications to problems of stress patterns, doubling rules in verb inflection , and the determination of the number of phonetic syllables corresponding to a written word are described. Computational linguistics differs in at least three essential respects from traditional linguistics. Foremost among these is that computational linguistics deals almost entirely with written languages. Because of this restriction to strictly reproducible forms and because of its direct connection with computers, it is both possible and necessary to operate primarily with operational definitions that are capable of machine implementation. Finally, the same forces that require strict operational definitions also impose upon us the necessity of establishing procedures of extremely high precision and accuracy. In a word, 80% is not nearly good enough for machine operation, 98% might pass, and it is fairly clear that programs will have to operate at well above the 99% level of accuracy if they are to attain any degree of general use. The attainment of such precision, and the proof that such precision has been obtained in a particular case, may well be considered primary problems in this area. If such precision is eventually to be obtained in the solution of such sweeping problems as machine translation , …