Non-Zipfian Distribution of Stopwords or Function Words and Subset Selection Models

Abstract

Stopwords and function words are relatively less informative for the content of a language and more often play a structural role in a sentence. Stopwords are ubiquitous words and may contain verbs, adjectives and adverbs. On the other hand, function words are strictly prepositions, conjunctions, pronouns, determiners, qualifiers, articles, interrogatives, and a limited number of auxiliary verbs. In contrast to the well known Zipf's law for rank-frequency plot for all words, the rank-frequency plots for stopwords or function words are best fitted by the Beta Rank Function (BRF). On the other hand, the rank-frequency plots of non-stopwords or non-function-words also deviate from the Zipf's law, but are better described by a quadratic function of log-token-count over log-rank than by BRF. Based on the observed rank of stopwords or function words in the full word list, we propose a stopword/function word/subset selection model that the probability for being selected, as a function of the word's rank $r$, is a decreasing Hill's function ($1/(1+(r/r_{mid})^\gamma)$); whereas the probability for not being selected is the standard Hill's function ($1/(1+(r_{mid}/r)^\gamma)$). We validate this selection probability model by a direct estimation from an independent collection of texts. We also show analytically that this model leads to a BRF rank-frequency distribution for stopwords or function words when the original full word list follows the Zipf's law, as well as explaining the quadratic fitting function for the non-stopwords or non-function-words. A corollary of these results is that Zipf's law is not expected to be true for telegraphic speech in early childhood language learners or in agrammatism patients.