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Standard models in quantitative linguistics assume that word usage follows a fixed frequency distribution, often Zipf's law or a close relative. This view, however, does not capture the near daily variations in topics of conversation, nor the short-term dynamics of language change. In order to understand the dynamics of human language use, we present a corpus of daily word frequency variation scraped from online news sources every 20 min for more than 2 years. We construct a simple time-varying model with a latent state, which is observed via word frequency counts. We use Bayesian techniques to infer the parameters of this model for 20,000 words, allowing us to convert complex word-frequency trajectories into low-dimensional parameters in word usage. By analyzing the inferred parameters of this model, we quantify the relative mobility and drift of words on a day-to-day basis, while accounting for sampling error. We quantify this variation and show evidence against "rich-get-richer" models of word use, which have been previously hypothesized to explain statistical patterns in language., (© 2022 Cognitive Science Society LLC.)

A major goal of linguistics and cognitive science is to understand what class of learning systems can acquire natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire many of the key structures present in natural language from positive evidence alone. We demonstrate this by providing the same learning model with data from 74 distinct formal languages which have been argued to capture key features of language, have been studied in experimental work, or come from an interesting complexity class. The model is able to successfully induce the latent system generating the observed strings from small amounts of evidence in almost all cases, including for regular (e.g., a n , [Formula: see text], and [Formula: see text]), context-free (e.g., [Formula: see text], and [Formula: see text]), and context-sensitive (e.g., [Formula: see text], and xx ) languages, as well as for many languages studied in learning experiments. These results show that relatively small amounts of positive evidence can support learning of rich classes of generative computations over structures. The model provides an idealized learning setup upon which additional cognitive constraints and biases can be formalized., Competing Interests: The authors declare no competing interest., (Copyright © 2022 the Author(s). Published by PNAS.)

The Pirahã language has been at the center of recent debates in linguistics, in large part because it is claimed not to exhibit recursion, a purported universal of human language. Here, we present an analysis of a novel corpus of natural Pirahã speech that was originally collected by Dan Everett and Steve Sheldon. We make the corpus freely available for further research. In the corpus, Pirahã sentences have been shallowly parsed and given morpheme-aligned English translations. We use the corpus to investigate the formal complexity of Pirahã syntax by searching for evidence of syntactic embedding. In particular, we search for sentences which could be analyzed as containing center-embedding, sentential complements, adverbials, complementizers, embedded possessors, conjunction or disjunction. We do not find unambiguous evidence for recursive embedding of sentences or noun phrases in the corpus. We find that the corpus is plausibly consistent with an analysis of Pirahã as a regular language, although this is not the only plausible analysis.

The frequency distribution of words has been a key object of study in statistical linguistics for the past 70 years. This distribution approximately follows a simple mathematical form known as Zipf's law. This article first shows that human language has a highly complex, reliable structure in the frequency distribution over and above this classic law, although prior data visualization methods have obscured this fact. A number of empirical phenomena related to word frequencies are then reviewed. These facts are chosen to be informative about the mechanisms giving rise to Zipf's law and are then used to evaluate many of the theoretical explanations of Zipf's law in language. No prior account straightforwardly explains all the basic facts or is supported with independent evaluation of its underlying assumptions. To make progress at understanding why language obeys Zipf's law, studies must seek evidence beyond the law itself, testing assumptions and evaluating novel predictions with new, independent data.

Absolute linguistic universals are often justified by cross-linguistic analysis: If all observed languages exhibit a property, the property is taken to be a likely universal, perhaps specified in the cognitive or linguistic systems of language learners and users. In many cases, these patterns are then taken to motivate linguistic theory. Here, we show that cross-linguistic analysis will very rarely be able to statistically justify absolute, inviolable patterns in language. We formalize two statistical methods--frequentist and Bayesian--and show that in both it is possible to find strict linguistic universals, but that the numbers of independent languages necessary to do so is generally unachievable. This suggests that methods other than typological statistics are necessary to establish absolute properties of human language, and thus that many of the purported universals in linguistics have not received sufficient empirical justification., (© 2013 Cognitive Science Society, Inc.)

We present a general information-theoretic argument that all efficient communication systems will be ambiguous, assuming that context is informative about meaning. We also argue that ambiguity allows for greater ease of processing by permitting efficient linguistic units to be re-used. We test predictions of this theory in English, German, and Dutch. Our results and theoretical analysis suggest that ambiguity is a functional property of language that allows for greater communicative efficiency. This provides theoretical and empirical arguments against recent suggestions that core features of linguistic systems are not designed for communication. (Contains 3 figures.)

The distribution of word orders across languages is highly nonuniform, with subject-verb-object (SVO) and subject-object-verb (SOV) orders being prevalent. Recent work suggests that the SOV order may be the default in human language. Why, then, is SVO order so common? We hypothesize that SOV/SVO variation can be explained by language users’ sensitivity to the possibility of noise corrupting the linguistic signal. In particular, the noisy-channel hypothesis predicts a shift from the default SOV order to SVO order for semantically reversible events, for which potential ambiguity arises in SOV order because two plausible agents appear on the same side of the verb. We found support for this prediction in three languages (English, Japanese, and Korean) by using a gesture-production task, which reflects word-order preferences largely independent of native language. Other patterns of crosslinguistic variation (e.g., the prevalence of case marking in SOV languages and its relative absence in SVO languages) also straightforwardly follow from the noisy-channel hypothesis. [ABSTRACT FROM PUBLISHER]

Sprouse and Almeida (S&A) present quantitative results that suggest that intuitive judgments utilised in syntax research are generally correct in two-condition comparisons: the sentence type that is presented as “good/grammatical” is usually rated better than the sentence type that is presented as “bad/ungrammatical” in controlled experiments. Although these evaluations of intuitive relative judgments are valuable, they do not justify the use of nonquantitative linguistic methods. We argue that objectivity is a universal value in science that should be adopted by linguistics. In addition, the reliability measures that S&A report are not sufficient for developing sophisticated linguistic theories. Furthermore, quantitative methods yield two additional benefits:consistency of judgmentsacross many pairs of judgments; and an understanding of therelative effect sizesacross sets of judgments. We illustrate these points with an experiment that demonstrates five clear levels of acceptability. Finally, we observe that S&A's experiments—where only two authors evaluated 10 years' worth of journal articles and one standard textbook within a few months—further emphasise one of our critical original points: conducting behavioural experiments is in many respectseasy and fastwith the advent of online research tools like Amazon's Mechanical Turk. Given the current ease of performing quantitative experiments (using a platform like Mechanical Turk) and the clear limitations of not doing so, linguistic hypotheses should be evaluated quantitatively whenever it is feasible. [ABSTRACT FROM PUBLISHER]

Recent evidence suggests that cognitive pressures associated with language acquisition and use could affect the organization of the lexicon. On one hand, consistent with noisy channel models of language (e.g., Levy, 2008), the phonological distance between wordforms should be maximized to avoid perceptual confusability (a pressure for dispersion). On the other hand, a lexicon with high phonological regularity would be simpler to learn, remember and produce (e.g., Monaghan et al., 2011) (a pressure for clumpiness). Here we investigate wordform similarity in the lexicon, using measures of word distance (e.g., phonological neighborhood density) to ask whether there is evidence for dispersion or clumpiness of wordforms in the lexicon. We develop a novel method to compare lexicons to phonotactically-controlled baselines that provide a null hypothesis for how clumpy or sparse wordforms would be as the result of only phonotactics. Results for four languages, Dutch, English, German and French, show that the space of monomorphemic wordforms is clumpier than what would be expected by the best chance model according to a wide variety of measures: minimal pairs, average Levenshtein distance and several network properties. This suggests a fundamental drive for regularity in the lexicon that conflicts with the pressure for words to be as phonologically distinct as possible. [ABSTRACT FROM AUTHOR]