Langage naturel

Résumé

Un langage naturel, ou langage ordinaire, est une langue « normale » parlée par un être humain. Il s'oppose au langage formel, tel que le langage informatique, ainsi qu'aux langues construites. Histoire histoire des langues On désigne par langage naturel le langage parlé par les humains, apparu entre et avant notre ère. Après 2 millions d'années de gestation par l’Homo erectus, qui a pris la suite de l'Homo habilis, l’homme moderne, appelé Homo sapiens, est apparu par la conjonction de nombreux facteurs :

La maîtrise du feu (environ avant notre ère), qui a permis d'alimenter un cerveau de taille croissante (au-dessus de ) ;
L’altricialité secondaire, qui a permis au nourrisson de passer de 60 % du cerveau à la naissance à moins de 25 % et d'atteindre, après de maturation, des volumes de cerveau de et plus ;
La préexistence d’une protolangue chantée par l'un des prédécesseurs de cette race, l'homme de Néandertal (the singing Neandertal), né il y a environ

Source officielle

https://fr.wikipedia.org/wiki/Langage_naturel

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Concepts associés (21)

vignette|Le drapeau des langues construites, représentant la tour de Babel. Une langue construite ou planifiée (ou idéolangue, parfois dénommée langue artificielle, ou conlang pour constructed languag

La grammaire est l'étude objective et systématique des éléments (phonème, morphème et mot) et des mécanismes et processus de formation, de construction et d'expression constitutifs d'une langue nat

La linguistique est une discipline scientifique s’intéressant à l’étude du langage. Elle n'est pas prescriptive mais descriptive. La prescription correspond à la norme, c'est-à-dire ce qui est jugé

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Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

Jie Luo, Mengshi Qi, Yiyu Wang, Yi Yang

With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent the whole video, while ignoring visual pattern consistencies and intrinsic temporal relationships across different frames. Furthermore, the semantic correspondence between natural language queries and person-centric actions in videos has not been fully explored. To address these problems, we propose a novel binary representation learning framework, named Semanticsaware Spatial-temporal Binaries (S(2)Bin), which simultaneously considers spatial-temporal context and semantic relationships for cross-modal video retrieval. By exploiting the semantic relationships between two modalities, S(2)Bin can efficiently and effectively generate binary codes for both videos and texts. In addition, we adopt an iterative optimization scheme to learn deep encoding functions with attribute-guided stochastic training. We evaluate our model on three video datasets and the experimental results demonstrate that S(2)Bin outperforms the state-of-the-art methods in terms of various cross-modal video retrieval tasks.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

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Further results on latent discourse models and word embeddings

Youssef Allouah

We discuss some properties of generative models for word embeddings. Namely, (Arora et al., 2016) proposed a latent discourse model implying the concentration of the partition function of the word vectors. This concentration phenomenon led to an asymptotic linear relation between the pointwise mutual information (PMI) of pairs of words and the scalar product of their vectors. Here, we first revisit this concentration phenomenon and prove it under slightly weaker assumptions, for a set of random vectors symmetrically distributed around the origin. Second, we empirically evaluate the relation between PMI and scalar products of word vectors satisfying the concentration property. Our empirical results indicate that, in practice, this relation does not hold with arbitrarily small error. This observation is further supported by two theoretical results: (i) the error cannot be exactly zero because the corresponding shifted PMI matrix cannot be positive semidefinite; (ii) under mild assumptions, there exist pairs of words for which the error cannot be close to zero. We deduce that either natural language does not follow the assumptions of the considered generative model, or the current word vector generation methods do not allow the construction of the hypothesized word embeddings.

MICROTOME PUBL2021

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EEG Spatiotemporal Patterns Underlying Self-other Voice Discrimination

Sixto Luis Alcoba Banqueri, Olaf Blanke, Giannina Rita Iannotti, Thomas Koenig, Pavo Orepic

There is growing evidence showing that the representation of the human “self” recruits special systems across different functions and modalities. Compared to self-face and self-body representations, few studies have investigated neural underpinnings specific to self-voice. Moreover, self-voice stimuli in those studies were consistently presented through air and lacking bone conduction, rendering the sound of self-voice stimuli different to the self-voice heard during natural speech. Here, we combined psychophysics, voice-morphing technology, and high-density EEG in order to identify the spatiotemporal patterns underlying self-other voice discrimination (SOVD) in a population of 26 healthy participants, both with air- and bone-conducted stimuli. We identified a self-voice-specific EEG topographic map occurring around 345 ms post-stimulus and activating a network involving insula, cingulate cortex, and medial temporal lobe structures. Occurrence of this map was modulated both with SOVD task performance and bone conduction. Specifically, the better participants performed at SOVD task, the less frequently they activated this network. In addition, the same network was recruited less frequently with bone conduction, which, accordingly, increased the SOVD task performance. This work could have an important clinical impact. Indeed, it reveals neural correlates of SOVD impairments, believed to account for auditory-verbal hallucinations, a common and highly distressing psychiatric symptom.

2021

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