The vocabulary of human languages has been argued to support efficient communication by optimizing the trade-off between complexity and informativeness (Kemp & Regier 2012). The argument has been based on cross-linguistic analyses of vocabulary in semantic domains of content words such as kinship, color, and number terms. The present work extends this analysis to a category of function words: indefinite pronouns (e.g. someone, anyone, no-one, cf. Haspelmath 2001). We build on previous work to establish the meaning space and featural make-up for indefinite pronouns, and show that indefinite pronoun systems across languages optimize the complexity/informativeness trade-off. This demonstrates that pressures for efficient communication shape both content and function word categories, thus tying in with the conclusions of recent work on quantifiers by Steinert-Threlkeld (2019). Furthermore, we argue that the trade-off may explain some of the universal properties of indefinite pronouns, thus reducing the explanatory load for linguistic theories.

The vocabulary of human languages has been argued to support efficient communication by optimizing the trade-off between simplicity and informativeness. The argument has been originally based on cross-linguistic analyses of vocabulary in semantic domains of content words, such as kinship, color, and number terms. The present work applies this analysis to a category of function words: indefinite pronouns (e.g., someone, anyone, no one). We build on previous work to establish the meaning space and featural make-up for indefinite pronouns, and show that indefinite pronoun systems across languages optimize the simplicity/informativeness trade-off. This demonstrates that pressures for efficient communication shape both content and function word categories. In doing so, our work aligns with several concurrent studies exploring the simplicity/informativeness trade-off in functional vocabulary. Importantly, we further argue that the trade-off may explain some of the universal properties of indefinite pronouns, thus reducing the explanatory load for linguistic theories.

We investigate the semantic knowledge of language models (LMs), focusing on (1) whether these LMs create categories of linguistic environments based on their semantic monotonicity properties, and (2) whether these categories play a similar role in LMs as in human language understanding, using negative polarity item licensing as a case study. We introduce a series of experiments consisting of probing with diagnostic classifiers (DCs), linguistic acceptability tasks, as well as a novel DC ranking method that tightly connects the probing results to the inner workings of the LM. By applying our experimental pipeline to LMs trained on various filtered corpora, we are able to gain stronger insights into the semantic generalizations that are acquired by these models.

Quantifying determiners most and more than half are standardly assumed to have the same truth-conditional meaning. Much work builds on this assumption in studying how the two quantifiers are mentally encoded and processed (Hackl, 2009; Lidz et al., 2011; Pietroski et al., 2009; Steinert-Threlkeld et al., 2015; Szymanik & Zajenkowski, 2010; Talmina et al., 2017). There is however empirical evidence that most is sometimes interpreted as ‘significantly more than half’ (Ariel, 2003, 2004; Ramotowska et al., 2020; Solt, 2011, 2016). Is this difference between most and more than half a pragmatic effect, or is the standard assumption that the two quantifiers are truth-conditionally equivalent wrong? We report two experiments which demonstrate that most preserves the ‘significantly more than half’ interpretation in negative environments, which we argue to speak in favor of there being a difference between the two quantifiers at the level of truth conditions.

Disjunctions in the scope of a possibility modal trigger so-called free choice inferences: (1a) gives rise to the inference (1b) (e.g., Kamp 1973).(1) a. John can read Article 1, Article 2, or Article 3.b. John can read Article 1, and he can read Article 2, and he can read Article 3.In the literature, such inferences are typically (but not always) derived as implicatures, crucially relying on the assumption that a sentence with a disjunction activates domain alternatives. To understand what domain alternatives are, note that a disjunction can be described by the set of elements (objects or propositions) that it covers. For the disjunction in (1a), that set of elements, which we will refer to as the domain D of disjunction, would be D ‫ס‬ ͕Article 1, Article 2, Article 3͖. Domain alternatives of a disjunction are other disjunctions that differ from the original in that they are constructed on smaller domains D′ ⊆ D.