The Syntax‐Semantics Interface

Hovav, Malka Rappaport; Levin, Beth

doi:10.1002/9781118882139.ch19

Cited by 21 publications

(1 citation statement)

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“…61 Certainly, from the viewpoint of the study of natural languages, multiple relations between both dimensions have been proposed, and what is known as "syntax-semantics interface" remains an active domain in the field (see Rappaport Hovav and Levin (2015) for an overview). Yet rather than eroding the frontier between both dimensions of language, those approaches presuppose it and intend to specify it.…”

Section: The Limits Of Semanticsmentioning

confidence: 99%

Why Can Computers Understand Natural Language?

Gastaldi

2020

Philos. Technol.

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The present paper intends to draw the conception of language implied in the technique of word embeddings that supported the recent development of deep neural network models in computational linguistics. After a preliminary presentation of the basic functioning of elementary artificial neural networks, we introduce the motivations and capabilities of word embeddings through one of its pioneering models, word2vec. To assess the remarkable results of the latter, we inspect the nature of its underlying mechanisms, which have been characterized as the implicit factorization of a word-context matrix. We then discuss the ordinary association of the "distributional hypothesis" with a "use theory of meaning", often justifying the theoretical basis of word embeddings, and contrast them to the theory of meaning stemming from those mechanisms through the lens of matrix models (such as VSMs and DSMs). Finally, we trace back the principles of their possible consistency through Harris's original distributionalism up to the structuralist conception of language of Saussure and Hjelmslev. Other than giving access to the technical literature and state of the art in the field of Natural Language Processing to non-specialist readers, the paper seeks to reveal the conceptual and philosophical stakes involved in the recent application of new neural network techniques to the computational treatment of language. Why can computers understand natural language?Il n'y a pas de "philosophie" du langage. Il n'y a que la linguistique.Louis Hjelmslev Principes de Grammaire Générale, 1928 1 I borrow this expression from Maniglier (2016, p. 359), who in turn takes inspiration from Deleuze's notion of "image of thought" (Deleuze, 1994, ch. III).2 See for instance Hale and Wright (1997). 3 See for instance Christopher Manning and Richard Socher's tutorial "Deep Learning for 23 Details of the different kinds of semantic and syntactic analogy relations can be found in Mikolov et al. (2013d); Schnabel et al. (2015).

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Section: The Limits Of Semanticsmentioning

confidence: 99%

Why Can Computers Understand Natural Language?

Gastaldi

2020

Philos. Technol.

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Kernel-Based Generative Adversarial Networks for Weakly Supervised Learning

Croce

Castellucci

Basili

2019

Lecture Notes in Computer Science

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Computational recognition of narratives

Hatavara,

Sandberg,

Andrushchenko

et al. 2024

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Computational recognition of narratives, if successful, would find innumerable applications with large digitized datasets. Systematic identification of narratives in the text flow could significantly contribute to such pivotal questions as where, when, and how narratives are employed. This paper discusses an approach to extract narratives from two datasets, Finnish parliamentary records (1980–2021) and oral history interviews with former Finnish MPs (1988–2018). Our study was based on an iterative approach, proceeding from original expert readings to a rule-based, computational approach that was elaborated with the help of annotated samples and annotation scheme. Annotated samples and computationally found extracts were compared, and a good correspondence was found. In this paper, we exhibit and compare the results from annotation and rule-based approach, and discuss examples of correctly and incorrectly found narrative sections. We consider that all attempts at recognizing and extracting narratives are definition dependent, and feed back to narrative theory.

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The Syntax‐Semantics Interface

Cited by 21 publications

References 91 publications

Why Can Computers Understand Natural Language?

Why Can Computers Understand Natural Language?

Kernel-Based Generative Adversarial Networks for Weakly Supervised Learning

Computational recognition of narratives

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