Journal of Quantitative Linguistics 

About: Journal of Quantitative Linguistics is an academic journal published by Routledge. The journal publishes majorly in the area(s): Zipf's law & Quantitative linguistics. It has an ISSN identifier of 0929-6174. Over the lifetime, 559 publications have been published receiving 7627 citations.

Good‐turing frequency estimation without tears*

Abstract: Linguists and speech researchers who use statistical methods often need to estimate the frequency of some type of item in a population containing items of various types. A common approach is to divide the number of cases observed in a sample by the size of the sample; sometimes small positive quantities are added to divisor and dividend in order to avoid zero estimates for types missing from the sample. These approaches are obvious and simple, but they lack principled justification, and yield estimates that can be wildly inaccurate. I.J. Good and Alan Turing developed a family of theoretically well-founded techniques appropriate to this domain. Some versions of the Good–Turing approach are very demanding computationally, but we define a version, the Simple Good–Turing estimator, which is straightforward to use. Tested on a variety of natural-language-related data sets, the Simple Good–Turing estimator performs well, absolutely and relative both to the approaches just discussed and to other, more sophisticated techniques.

Cutting the Gordian Knot: The Moving-Average Type–Token Ratio (MATTR)

Abstract: Type–token ratio (TTR), or vocabulary size divided by text length (V/N), is a time-honoured but unsatisfactory measure of lexical diversity. The problem is that the TTR of a text sample is affected by its length. We present an algorithm for rapidly computing TTR through a moving window that is independent of text length, and we demonstrate that this measurement can detect changes within a text as well as differences between texts.

Two Regimes in the Frequency of Words and the Origins of Complex Lexicons: Zipf’s Law Revisited∗

Abstract: Zipf’s law states that the frequency of a word is a power function of its rank. The exponent of the power is usually accepted to be close to (-)1. Great deviations between the predicted and real nu...

Binomial Confidence Intervals and Contingency Tests: Mathematical Fundamentals and the Evaluation of Alternative Methods

Abstract: Many statistical methods rely on an underlying mathematical model of probability based on a simple approximation, one that is simultaneously well-known and yet frequently misunderstood. The Normal approximation to the Binomial distribution underpins a range of statistical tests and methods, including the calculation of accurate confidence intervals, performing goodness of fit and contingency tests, line- and model-fitting, and computational methods based upon these. A common mistake is in assuming that, since the probable distribution of error about the “true value” in the population is approximately Normally distributed, the same can be said for the error about an observation. This paper is divided into two parts: fundamentals and evaluation. First, we examine the estimation of confidence intervals using three initial approaches: the “Wald” (Normal) interval, the Wilson score interval and the “exact” Clopper-Pearson Binomial interval. Whereas the first two can be calculated directly from formula...

Evaluation in Media Discourse Analysis of a Newspaper Corpus

Abstract: Monika Bednarek, Evaluation in Media Discourse Analysis of a Newspaper Corpus. London/New York: Continuum, 2006. ISBN: 9780826491268 (hbk), pp. xvi + 253. Evaluation or the linguistic expression of...