Don't Blame Distributional Semantics if it can't do Entailment

Distributional semantics has had enormous empirical success in Computational Linguistics and Cognitive Science in modeling various semantic phenomena, such as semantic similarity, and distributional models are widely used in state-of-the-art Natural Language Processing systems. However, the theoretical status of distributional semantics within a broader theory of language and cognition is still unclear: What does distributional semantics model? Can it be, on its own, a fully adequate model of the meanings of linguistic expressions? The standard answer is that distributional semantics is not fully adequate in this regard, because it falls short on some of the central aspects of formal semantic approaches: truth conditions, entailment, reference, and certain aspects of compositionality. We argue that this standard answer rests on a misconception: These aspects do not belong in a theory of expression meaning, they are instead aspects of speaker meaning, i.e., communicative intentions in a particular context. In a slogan: words do not refer, speakers do. Clearing this up enables us to argue that distributional semantics on its own is an adequate model of expression meaning. Our proposal sheds light on the role of distributional semantics in a broader theory of language and cognition, its relationship to formal semantics, and its place in computational models.Comment: To appear in Proceedings of the 13th International Conference on Computational Semantics (IWCS 2019), Gothenburg, Swede

The interaction between cognitive ease and informativeness shapes the lexicons of natural languages

Lexical ambiguity is pervasive in language, and often systematic. Previous work shows that systematic ambiguities involve related meanings. This is attributed to cognitive pressure towards simplicity in language, as it makes lexicons easier to learn and use. The present study examines the interplay between this pressure and competing pressure for languages to support accurate information transfer. We hypothesize that ambiguity is shaped by a balance of the two pressures; and find support for this idea in data from over 1200 languages and 1400 meanings. Our results thus suggest that universal forces shape the lexicons of natural languages

Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming

Often we can use different names to refer to the same object (e.g., pony vs. horse) and naming choices vary among people. In the present study we explore factors that affect naming variation for visually presented objects. We analyse a large dataset of object naming with realistic images and focus on two factors: (a) the visual typicality of objects and their context for the names used by human annotators and (b) the lexical frequency of these names. We use a novel computational approach to estimate visual typicality by calculating the visual similarity of a given object (or context) and the average visual information of other objects which were given the same name (in an independent dataset). In difference to previous studies, we not only consider the name used by most annotators for a given object (top name) but explore also the role of the second most frequently used name (alternative name). Our results show that naming variation decreases the more typical an object is for its top name and the higher the lexical frequency of this name. For alternative names the opposite is found. Context typicality does not show a general effect in our analysis. Overall our results show that visual and lexical characteristics relating to name candidates beyond the top name are informative for predicting variability in object naming. On a methodological level, our results demonstrate the potential of using large scale datasets with realistic images in conjunction with computational methods to inform models of human object naming

Ethnic adjectives are proper adjectives

In this paper, we challenge the view that ethnic adjectives (EAs) are nouns in disguise. Instead, we propose a unified semantics of the thematic and classificatory uses of EAs that treats them as proper adjectives, but nonetheless accounts for the phenomena that led to their analysis as nouns.Peer ReviewedPostprint (published version