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Publication . Article . 2018

Language comprehension as a multi-label classification problem

Konstantin Sering; Petar Milin; R. Harald Baayen;
Open Access
Published: 19 Apr 2018 Journal: Statistica Neerlandica, volume 72, pages 339-353 (issn: 0039-0402, Copyright policy )
Publisher: Wiley
Abstract

The initial stage of language comprehension is a multi-label classification problem. Listeners or readers, presented with an utterance, need to discriminate between the intended words and the tens of thousands of other words they know. We propose to address this problem by pairing a network trained with the learning rule of Rescorla andWagner (1972) with a second network trained independently with the learning rule of Widrow and Hoff (1960). The first network has to recover from sublexical input features the meanings encoded in the language signal, resulting in a vector of activations over the lexicon. The second network takes this vector as input and further reduces uncertainty about the intended message. Classification performance for a lexicon with 52,000 entries is good. The model also correctly predicts several aspects of human language comprehension. By rejecting the traditional linguistic assumption that language is a (de)compositional system, and by instead espousing a discriminative approach (Ramscar, 2013), a more parsimonious yet highly effective functional characterization of the initial stage of language comprehension is obtained.

Subjects by Vocabulary

Microsoft Academic Graph classification: Natural language processing computer.software_genre computer Utterance Comprehension Artificial intelligence business.industry business SIGNAL (programming language) Learning rule Characterization (mathematics) Discriminative model Multi-label classification Lexicon Computer science

Subjects

Statistics, Probability and Uncertainty, Statistics and Probability

Related Organizations
Funded by
EC| WIDE
Project
WIDE
Wide Incremental learning with Discrimination nEtworks
  • Funder: European Commission (EC)
  • Project Code: 742545
  • Funding stream: H2020 | ERC | ERC-ADG
Related to Research communities
Digital Humanities and Cultural Heritage
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