How to marry a star: probabilistic constraints for meaning in context

• URL: http://arxiv.org/abs/2009.07936v3
• Date: Mon, 12 Sep 2022 10:22:36 GMT
• Title: How to marry a star: probabilistic constraints for meaning in context
• Authors: Katrin Erk, Aurelie Herbelot
• Abstract summary: We derive a notion of 'word meaning in context' that characterizes meaning as both intensional and conceptual. We introduce a framework for specifying local as well as global constraints on word meaning in context, together with their interactions. We represent sentence meaning as a'situation description system', a probabilistic model which takes utterance understanding to be the mental process of describing to oneself one or more situations that would account for an observed utterance.
• Score: 11.670687428360688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we derive a notion of 'word meaning in context' that characterizes meaning as both intensional and conceptual. We introduce a framework for specifying local as well as global constraints on word meaning in context, together with their interactions, thus modelling the wide range of lexical shifts and ambiguities observed in utterance interpretation. We represent sentence meaning as a 'situation description system', a probabilistic model which takes utterance understanding to be the mental process of describing to oneself one or more situations that would account for an observed utterance. We show how the system can be implemented in practice, and apply it to examples containing various contextualisation phenomena.

Related papers

• To Word Senses and Beyond: Inducing Concepts with Contextualized Language Models [0.9176056742068812]
  Polysemy and synonymy are crucial facets of lexical ambiguity. In this paper, we introduce Concept Induction, the unsupervised task of learning a soft clustering among words. We propose a bi-level approach to Concept Induction that leverages both a local lemma-centric view and a global cross-lexicon perspective.
  arXiv  Detail & Related papers  (2024-06-28T17:07:06Z)
• A Note on an Inferentialist Approach to Resource Semantics [48.65926948745294]
  'Inferentialism' is the view that meaning is given in terms of inferential behaviour. This paper shows how 'inferentialism' enables a versatile and expressive framework for resource semantics.
  arXiv  Detail & Related papers  (2024-05-10T14:13:21Z)
• Iconic Gesture Semantics [87.00251241246136]
  Informational evaluation is spelled out as extended exemplification (extemplification) in terms of perceptual classification of a gesture's visual iconic model. We argue that the perceptual classification of instances of visual communication requires a notion of meaning different from Frege/Montague frameworks. An iconic gesture semantics is introduced which covers the full range from gesture representations over model-theoretic evaluation to inferential interpretation in dynamic semantic frameworks.
  arXiv  Detail & Related papers  (2024-04-29T13:58:03Z)
• A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches [7.310850880167243]
  Aspectual meaning refers to how the internal temporal structure of situations is presented. This survey gives an overview of computational approaches to modeling lexical and grammatical aspect.
  arXiv  Detail & Related papers  (2022-08-18T18:22:42Z)
• Latent Topology Induction for Understanding Contextualized Representations [84.7918739062235]
  We study the representation space of contextualized embeddings and gain insight into the hidden topology of large language models. We show there exists a network of latent states that summarize linguistic properties of contextualized representations.
  arXiv  Detail & Related papers  (2022-06-03T11:22:48Z)
• FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations [99.54048050189971]
  We present a framework for learning new visual concepts quickly, guided by multiple naturally occurring data streams. The learned concepts support downstream applications, such as answering questions by reasoning about unseen images. We demonstrate the effectiveness of our model on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-03-30T19:45:00Z)
• Theoretical foundations and limits of word embeddings: what types of meaning can they capture? [0.0]
  Measuring meaning is a central problem in cultural sociology. I theorize the ways in which word embeddings model three core premises of a structural linguistic theory of meaning.
  arXiv  Detail & Related papers  (2021-07-22T00:40:33Z)
• Understanding Synonymous Referring Expressions via Contrastive Features [105.36814858748285]
  We develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels. We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-20T17:56:24Z)
• On the Faithfulness Measurements for Model Interpretations [100.2730234575114]
  Post-hoc interpretations aim to uncover how natural language processing (NLP) models make predictions. To tackle these issues, we start with three criteria: the removal-based criterion, the sensitivity of interpretations, and the stability of interpretations. Motivated by the desideratum of these faithfulness notions, we introduce a new class of interpretation methods that adopt techniques from the adversarial domain.
  arXiv  Detail & Related papers  (2021-04-18T09:19:44Z)
• Semantic Holism and Word Representations in Artificial Neural Networks [0.0]
  We show that word representations from the Skip-gram variant of the word2vec model exhibit interesting semantic properties. This is usually explained by referring to the general distributional hypothesis. We propose a more specific approach based on Frege's holistic and functional approach to meaning.
  arXiv  Detail & Related papers  (2020-03-11T21:04:49Z)
• Interpreting Interpretations: Organizing Attribution Methods by Criteria [40.812424038838984]
  In this work we expand the foundationsof human-understandable concepts with which attributionscan is interpreted. We incorporate the logical concepts of necessity andsufficiency, and the concept of proportionality. We evaluate our measures on a collection of methods explaining convolutional neural networks (CNN) for image classification.
  arXiv  Detail & Related papers  (2020-02-19T03:37:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.