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• W2588474037 abstract "Defining New Words in Corpus Data: Productivity of English Suffixes in the British National Corpus Eiji Nishimoto (enishimoto@gc.cuny.edu) Ph.D. Program in Linguistics, The Graduate Center The City University of New York 365 Fifth Avenue, New York, NY 10016 USA Abstract The present study introduces a method of identifying potentially new words in a large corpus of texts, and assesses the morphological productivity of 12 English suffixes, based on some 78 million words of the written component (books and periodicals) of the British National Corpus (BNC). The method compares two corpus segments (created by randomly sampling at the level of documents within the BNC), and defines new words as those that are not shared across segments (segments being interpreted as randomly sampled speaker groups). The approach taken differs from others in the literature in that new words are identified irrespective of how many times a given word is used by the same speaker (author). A productivity ranking of the 12 English suffixes is obtained, and the results are shown to be intuitively satisfying and stable over different sample sizes. With a psycholinguistic interpretation of the data, implications for the nature of intuitions about productivity are considered. Introduction Morphological productivity is central to the study of word formation, but it continues to defy a solid, uniform description (see e.g., Aronoff, 1976; Bauer, 2001; Plag, 1999). The coinage of a “new” word is abundant in our daily use of language; for example, a person who is being gossiped about may be referred to as a gossipee, or a used book may be cleanish. Affixation in English (as in gossip + -ee gossipee; clean + -ish cleanish) is a productive word formation process, and there is plenty of evidence that affixes differ in their degree of productivity (e.g., Aronoff, 1976; Bauer, 2001); for example, words can in general be formed more easily with -ness than with -ity (and thus we may accept cleanness but not cleanity). The majority of researchers investigating the issue of productivity are interested in accounting for varying degrees of productivity, and several productivity measures have been proposed in the literature (e.g., Aronoff, 1976; Baayen, 1992, 2001; Bauer, 2001; Plag, 1999). Assessing the degree of productivity, however, has proven to be a complex task (Bauer, 2001): while the consensus seems to be that capturing the coinage of new words is essential in assessing productivity, there is an inherent difficulty in defining what a “new” word is. Most notable among previous studies is a corpus-based approach proposed by Baayen (1992, 2001). Based on word frequency in a large corpus of texts, his productivity measure is formulated as P = n 1 /N, where given a particular affix, n 1 is the number of word types with that affix that occur only once (the so-called hapax legomena, hereafter hapaxes), N is the sum of word tokens with that affix, and P is the productivity index. 1 P is interpreted as expressing the probability of encountering a word type with a given affix that has not been seen in the sampled corpus. Thus, new words are defined under this measure as “unseen” words in a corpus. An important characteristic of P is that it is based on token frequency—N directly refers to a count over tokens, and a word is included in the n 1 count only if it occurs just once. The measure P, with its focus on hapaxes as estimators of unseen words, is motivated by the probability estimation method of Good (1953)—or the Good-Turing estimation method (Church & Gale, 1991). 2 While a dictionary provides another source of data for quantifying morphological productivity, a corpus-based approach has many advantages. A large corpus of texts contains productively formed words that are typically not listed in a dictionary (e.g., gossipee), and corpus data reflect how words are actually used (Baayen & Lieber, 1991; Baayen & Renouf, 1996). The present study pursues and extends the corpus-based approach by introducing a new method of identifying new words and assessing productivity. Type Frequency and Deleted Estimation It has been suggested that the type frequency for an affix (the number of word types with an affix) in a corpus, represented by V, is inadequate in expressing its degree of productivity. Baayen and Lieber (1991: 804) point out that in their reference corpus of 18 million words, the type frequencies for -ness (497) and -ity (405) do not adequately express the fact that -ness is intuitively felt to be much more productive than -ity. They find that the P indices for -ness (0.0044) and -ity (0.0007) are more in line with linguists’ intuitive estimates for these suffixes. There are, however, some aspects of the measure P that can be quite counter- intuitive. In Baayen and Lieber (1991), for example, the P index for verbal suffix -ize (0.00007) is substantially lower As is usually the case in a corpus study, the term token refers to each occurrence of a word, and the term type refers to each distinct word. For instance, if we have {awareness, fairness, fairness, sharpness, sharpness}, the token frequency for -ness is 5 (the sum of all occurrences of -ness), whereas the type frequency for -ness is 3 (the number of distinct words with -ness). For more detail, see Baayen (2001)." @default.
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• W2588474037 title "Defining New Words in Corpus Data: Productivity of English Suffixes in the British National Corpus" @default.
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