Corpus

Résumé

Un corpus est un ensemble de documents, artistiques ou non (textes, s, vidéos), regroupés dans une optique précise. On peut utiliser des corpus dans plusieurs domaines : études littéraires, linguistiques, scientifiques, philosophie Genres de corpus Le corpus en linguistique La branche de la linguistique qui se préoccupe plus spécifiquement des corpus s'appelle logiquement la linguistique de corpus. Elle est liée au développement des systèmes informatiques, en particulier à la constitution de bases de données textuelles. Depuis 2009, la revue universitaire CORPUS est dédiée à ce domaine. On parle de corpus pour désigner l'aspect normatif de la langue : sa structure et son code en particulier. « Corpus » est généralement opposé à « status » (ou statut), qui correspond aux conditions d'utilisation de la langue. Cette opposition est commune dans l'étude des politiques linguistiques. Le corpus en littérature Le corpus regroupe un ensemble de textes ayan

Source officielle

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

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The objective of this course is to present the main models, formalisms and algorithms necessary for the development of applications in the field of natural language information processing. The concepts introduced during the lectures will be applied during practical sessions.

This course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities circle of processing and interpretation, from data acquisition to new understandings.

This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as images, music and social network data.

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Séances de cours associées (16)

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Extracting Directional and Comparable Corpora from a Multilingual Corpus for Translation Studies

Thomas Meyer

Translation studies rely more and more on corpus data to examine specificities of translated texts, that can be translated from different original languages and compared to original texts. In parallel, more and more multilingual corpora are becoming available for various natural language processing tasks. This paper questions the use of these multilingual corpora in translation studies and shows the methodological steps needed in order to obtain more reliably comparable sub-corpora that consist of original and directly translated text only. Various experiments are presented that show the advantage of directional sub-corpora.

2012

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Algorithmic composition of melodies with deep recurrent neural networks

Florian François Colombo

A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus of melodies and turned into automated mu- sic composers able to generate new melodies coherent with the style they have been trained on. We employ gated-recurrent unit (GRU) networks that have been shown to be particularly efficient in learning complex sequential activations with arbitrary long time lags. Our model processes rhythm and melody in parallel while modeling the relation between these two properties. Using such an approach, we were able to generate interesting complete melodies or suggest possible continuations of a melody fragment that is coherent with the characteristics of the fragment itself.

2016

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Modeling the Impact of Modifiers on Emotional Statements

Margarita Bolívar Jiménez, Pearl Pu Faltings, Valentina Sintsova

Humans use a variety of modifiers to enrich communications with one another. While this is a deliberate subtlety in our language, the presence of modifiers can cause problems for emotion analysis by machines. Our research objective is to understand and compare the influence of different modifiers on a wide range of emotion categories. We propose a novel data analysis method that not only quantifies how much emotional statements change under each modifier, but also models how emotions shift and how their confidence changes. This method is based on comparing the distributions of emotion labels for modified and non-modified occurrences of emotional terms within labeled data. We apply this analysis to study six types of modifiers (negation, intensification, conditionality, tense, interrogation, and modality) within a large corpus of tweets with emotional hashtags. Our study sheds light on how to model negation relations between given emotions, reveals the impact of previously under-studied modifiers, and suggests how to detect more precise emotional statements.

SPRINGER NATURE SWITZERLAND AG2018

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