no code implementations • NAACL (CMCL) 2021 • Franck Dary, Alexis Nasr, Abdellah Fourtassi
In this paper we describe our contribution to the CMCL 2021 Shared Task, which consists in predicting 5 different eye tracking variables from English tokenized text.
no code implementations • NAACL (SUKI) 2022 • Sebastien Montella, Lina Rojas-Barahona, Frederic Bechet, Johannes Heinecke, Alexis Nasr
In general, QA systems query a Knowledge Base (KB) to detect and extract the raw answers as final prediction.
no code implementations • ACL (IWPT) 2021 • Franck Dary, Alexis Nasr
The Reading Machine, is a parsing framework that takes as input raw text and performs six standard nlp tasks: tokenization, pos tagging, morphological analysis, lemmatization, dependency parsing and sentence segmentation.
1 code implementation • 21 Mar 2024 • Hichem Ammar Khodja, Frédéric Béchet, Quentin Brabant, Alexis Nasr, Gwénolé Lecorvé
To study this task, we present WikiFactDiff, a dataset that describes the evolution of factual knowledge between two dates as a collection of simple facts divided into three categories: new, obsolete, and static.
no code implementations • 12 Feb 2023 • Sebastien Montella, Alexis Nasr, Johannes Heinecke, Frederic Bechet, Lina M. Rojas-Barahona
Text generation from Abstract Meaning Representation (AMR) has substantially benefited from the popularized Pretrained Language Models (PLMs).
no code implementations • 28 Jun 2022 • Franck Dary, Maxime Petit, Alexis Nasr
Greedy algorithms for NLP such as transition based parsing are prone to error propagation.
no code implementations • COLING 2020 • Cindy Aloui, Carlos Ramisch, Alexis Nasr, Lucie Barque
Contextualised embeddings such as BERT have become de facto state-of-the-art references in many NLP applications, thanks to their impressive performances.
no code implementations • WS 2019 • Frederic Bechet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, Alexis Nasr, Frederic Herledan
Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document.
no code implementations • 20 Sep 2019 • Erfan Miahi, Seyed Abolghasem Mirroshandel, Alexis Nasr
Every individual of the genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome), and its fitness is calculated by a novel proposed method named GeNAS-WF especially designed for noisy, low resolution, and imbalanced datasets.
no code implementations • JEPTALNRECITAL 2019 • Frederic Bechet, Cindy Aloui, Delphine Charlet, Geraldine Damnati, Johannes Heinecke, Alexis Nasr, Frederic Herledan
Le but de cette {\'e}tude est de permettre le d{\'e}veloppement de telles ressources pour d{'}autres langues {\`a} moindre co{\^u}t en proposant une m{\'e}thode g{\'e}n{\'e}rant de mani{\`e}re semi-automatique des questions {\`a} partir d{'}une analyse s{\'e}mantique d{'}un grand corpus.
no code implementations • NAACL 2019 • Manon Scholivet, Franck Dary, Alexis Nasr, Benoit Favre, Carlos Ramisch
The existence of universal models to describe the syntax of languages has been debated for decades.
no code implementations • 21 Dec 2018 • Gabriel Marzinotto, Frédéric Béchet, Géraldine Damnati, Alexis Nasr
This paper describes a Semantic Frame parsing System based on sequence labeling methods, precisely BiLSTM models with highway connections, for performing information extraction on a corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism.
no code implementations • LREC 2018 • Gabriel Marzinotto, Jeremy Auguste, Frederic Bechet, Géraldine Damnati, Alexis Nasr
This paper presents a publicly available corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism.
no code implementations • JEPTALNRECITAL 2018 • Robin Perrotin, Alexis Nasr, Jeremy Auguste
Les conversations techniques en ligne sont un type de productions linguistiques qui par de nombreux aspects se d{\'e}marquent des objets plus usuellement {\'e}tudi{\'e}s en traitement automatique des langues : il s{'}agit de dialogues {\'e}crits entre deux locuteurs qui servent de support {\`a} la r{\'e}solution coop{\'e}rative des probl{\`e}mes des usagers.
no code implementations • JEPTALNRECITAL 2018 • S{\'e}bastien Delecraz, Leonor Becerra-Bonache, Beno{\^\i}t Favre, Alexis Nasr, Fr{\'e}d{\'e}ric Bechet
La d{\'e}sambigu{\"\i}sation des rattachements pr{\'e}positionnels est une t{\^a}che syntaxique qui demande des connaissances s{\'e}mantiques, pouvant {\^e}tre extraites d{'}une image associ{\'e}e au texte trait{\'e}.
no code implementations • WS 2017 • Sebastien Delecraz, Alexis Nasr, Frederic Bechet, Benoit Favre
PP-attachments are an important source of errors in parsing natural language.
no code implementations • EMNLP 2017 • Jungo Kasai, Bob Frank, Tom McCoy, Owen Rambow, Alexis Nasr
We present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled attachment scores.
no code implementations • COLING 2016 • Olivier Michalon, Corentin Ribeyre, C, Marie ito, Alexis Nasr
Syntax plays an important role in the task of predicting the semantic structure of a sentence.
no code implementations • LREC 2016 • Carlos Ramisch, Alexis Nasr, Andr{\'e} Valli, Jos{\'e} Deulofeu
We introduce DeQue, a lexicon covering French complex prepositions (CPRE) like {``}{\`a} partir de{''} (from) and complex conjunctions (CCONJ) like {``}bien que{''} (although).
no code implementations • LREC 2014 • Alexis Nasr, Frederic Bechet, Benoit Favre, Thierry Bazillon, Jose Deulofeu, Andre Valli
Syntactic parsing of speech transcriptions faces the problem of the presence of disfluencies that break the syntactic structure of the utterances.
no code implementations • JEPTALNRECITAL 2013 • Ahmed Hamdi, Rahma Boujelbane, Nizar Habash, Alexis Nasr
no code implementations • LREC 2012 • Thierry Bazillon, Melanie Deplano, Frederic Bechet, Alexis Nasr, Benoit Favre
This paper describes the syntactic annotation process of the DECODA corpus.