147 research outputs found
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
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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
The Impact of Familiarity on Naming Variation: A Study on Object Naming in Mandarin Chinese
Different speakers often produce different names for the same object or
entity (e.g., "woman" vs. "tourist" for a female tourist). The reasons behind
variation in naming are not well understood. We create a Language and Vision
dataset for Mandarin Chinese that provides an average of 20 names for 1319
naturalistic images, and investigate how familiarity with a given kind of
object relates to the degree of naming variation it triggers across subjects.
We propose that familiarity influences naming variation in two competing ways:
increasing familiarity can either expand vocabulary, leading to higher
variation, or promote convergence on conventional names, thereby reducing
variation. We find evidence for both factors being at play. Our study
illustrates how computational resources can be used to address research
questions in Cognitive Science
Comparing models of pronoun production and interpretation via observational and experimental evidence
Pronouns like she are frequently produced by speakers to refer to entities in discourse. For communication to be successful, comprehenders must be able to interpret these pronouns by identifying the appropriate referent. In the existing literature, three main models of pronoun production and interpretation have been proposed. These models have traditionally been tested through story continuation tasks, using carefully designed stimuli. In our study, we take a different approach by utilizing naturalistic passages from corpora, in two analyses, one observational and one experimental. Our analyses support the Bayesian model. In this model and in our experimental data, the relationship between pronoun production and interpretation can be captured using Bayes' rule. Specifically, pronoun interpretation is affected both by the probability that the referent will be mentioned next and by the probability that a pronoun will be used to refer to that referent. Moreover, both observational and experimental data provide evidence that pronoun production biases are insensitive to semantic and pragmatic factors – here, discourse relations – which do affect pronoun interpretation, in line with the prediction of the so-called strong form of the Bayesian Model
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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
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