Search Results for author: Marco Dinarelli

Found 23 papers, 1 papers with code

Multi-Task Sequence Prediction For Tunisian Arabizi Multi-Level Annotation

no code implementations10 Nov 2020 Elisa Gugliotta, Marco Dinarelli, Olivier Kraif

We show also how we used the system in order to annotate a Tunisian Arabizi corpus, which has been afterwards manually corrected and used to further evaluate sequence models on Tunisian data.

POS Text Classification +1

TArC. Un corpus d'arabish tunisien

no code implementations JEPTALNRECITAL 2020 Elisa Gugliotta, Marco Dinarelli

TArC : Incrementally and Semi-Automatically Collecting a Tunisian arabish Corpus This article describes the collection process of the first morpho-syntactically annotated Tunisian arabish Corpus (TArC).

TArC: Incrementally and Semi-Automatically Collecting a Tunisian Arabish Corpus

no code implementations LREC 2020 Elisa Gugliotta, Marco Dinarelli

This article describes the constitution process of the first morpho-syntactically annotated Tunisian Arabish Corpus (TArC).

Hybrid Neural Models For Sequence Modelling: The Best Of Three Worlds

no code implementations16 Sep 2019 Marco Dinarelli, Loïc Grobol

We propose a neural architecture with the main characteristics of the most successful neural models of the last years: bidirectional RNNs, encoder-decoder, and the Transformer model.

Mod\`eles neuronaux hybrides pour la mod\'elisation de s\'equences : le meilleur de trois mondes ()

no code implementations JEPTALNRECITAL 2019 Marco Dinarelli, Lo{\"\i}c Grobol

Nous proposons une architecture neuronale avec les caract{\'e}ristiques principales des mod{\`e}les neuronaux de ces derni{\`e}res ann{\'e}es : les r{\'e}seaux neuronaux r{\'e}currents bidirectionnels, les mod{\`e}les encodeur-d{\'e}codeur, et le mod{\`e}le Transformer.

Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling

no code implementations9 Apr 2019 Marco Dinarelli, Loïc Grobol

During the last couple of years, Recurrent Neural Networks (RNN) have reached state-of-the-art performances on most of the sequence modelling problems.

Effective Spoken Language Labeling with Deep Recurrent Neural Networks

no code implementations20 Jun 2017 Marco Dinarelli, Yoann Dupont, Isabelle Tellier

Understanding spoken language is a highly complex problem, which can be decomposed into several simpler tasks.

Spoken Language Understanding

Label-Dependencies Aware Recurrent Neural Networks

no code implementations6 Jun 2017 Yoann Dupont, Marco Dinarelli, Isabelle Tellier

In this work we propose a solution far simpler but very effective: an evolution of the simple Jordan RNN, where labels are re-injected as input into the network, and converted into embeddings, in the same way as words.

Spoken Language Understanding

R\'eseaux neuronaux profonds pour l'\'etiquetage de s\'equences (Deep Neural Networks for Sequence Labeling)

no code implementations JEPTALNRECITAL 2017 Yoann Dupont, Marco Dinarelli, Isabelle Tellier

R{\'e}cemment, une variante de r{\'e}seau neuronal particuli{\`e}rement adapt{\'e} {\`a} l{'}{\'e}tiquetage de s{\'e}quences textuelles a {\'e}t{\'e} propos{\'e}e, utilisant des repr{\'e}sentations distributionnelles des {\'e}tiquettes.

D\'etection des mots non-standards dans les tweets avec des r\'eseaux de neurones (Detecting non-standard words in tweets with neural networks)

no code implementations JEPTALNRECITAL 2017 Tian Tian, Isabelle Tellier, Marco Dinarelli, Pedro Cardoso

Dans cet article, nous proposons un mod{\`e}le pour d{\'e}tecter dans les textes g{\'e}n{\'e}r{\'e}s par des utilisateurs (en particulier les tweets), les mots non-standards {\`a} corriger.

Improving Recurrent Neural Networks For Sequence Labelling

no code implementations8 Jun 2016 Marco Dinarelli, Isabelle Tellier

In this paper we study different types of Recurrent Neural Networks (RNN) for sequence labeling tasks.

POS Spoken Language Understanding

Evaluation of different strategies for domain adaptation in opinion mining

no code implementations LREC 2014 Garcia-Fern, Anne ez, Olivier Ferret, Marco Dinarelli

The work presented in this article takes place in the field of opinion mining and aims more particularly at finding the polarity of a text by relying on machine learning methods.

Domain Adaptation Opinion Mining +2

Tree-Structured Named Entity Recognition on OCR Data: Analysis, Processing and Results

no code implementations LREC 2012 Marco Dinarelli, Sophie Rosset

We evaluate our procedure for preprocessing OCR-ized data in two ways: in terms of perplexity and OOV rate of a language model on development and evaluation data, and in terms of the performance of the named entity detection system on the preprocessed data.

Language Modelling Named Entity Recognition +1

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