Abstract:The aim of this paper is to integrate spatial cognition with lexical semantics. We develop cognitive models of actions and events based on conceptual spaces and vectors on them. The models are then used to present a semantic theory of verbs.We propose a two-vector model of events including a force vector and a result vector. We argue that our framework provides a unified account for a multiplicity of linguistic phenomena related to verbs. Among other things it provides a cognitive explanation for the lexico-semantic constraint regarding manner vs. result and for polysemy caused by intentionality. It also generates a unified definition of aspect.
<p>We argue for a purely pragmatic account of the ignorance inferences associated with superlative but not comparative modifiers (at least vs. more than). Ignorance inferences for both modifiers are triggered when the question under discussion (QUD) requires an exact answer, but when these modifiers are used out of the blue the QUD is implicitly reconstructed based on the way these modifiers are typically used, and on the fact that "at least n", but not "more than n", mentions and does not exclude the lower bound "exactly n". The paper presents new experimental evidence for the context-sensitivity of ignorance inferences, and also for the hypothesis that the higher processing cost reported in the literature for superlative modifiers is context-dependent in the exact same way.</p><p>Keywords: superlative vs. comparative modifiers, ignorance inferences, questions under discussion, experimental semantics and pragmatics</p>
This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. It is a simple, standard model with one key innovation, an entity library. Our results show that this innovation greatly facilitates the identification of infrequent characters. Because of the generic nature of our model, this finding is potentially relevant to any task that requires effective learning from sparse or unbalanced data.
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.
Humans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations have been proposed. Such entity-centric models have shown empirical success, but we still know little about why.In this paper we analyze the behavior of two recently proposed entity-centric models in a referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4). We show that these models outperform the state of the art on this task, and that they do better on lower frequency entities than a counterpart model that is not entity-centric, with the same model size. We argue that making models entitycentric naturally fosters good architectural decisions. However, we also show that these models do not really build entity representations and that they make poor use of linguistic context. These negative results underscore the need for model analysis, to test whether the motivations for particular architectures are borne out in how models behave when deployed.1 Note the analogy with traditional models in formal linguistics like Discourse Representation Theory (Kamp and Reyle, 2013). 2 Source code for our model, the training procedure and the new dataset is published on https://github.com/ amore-upf/analysis-entity-centric-nns.3 https://competitions.codalab.org/ competitions/17310.
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