1 code implementation • ACL 2022 • Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois
There are two main paradigms for Named Entity Recognition (NER): sequence labelling and span classification.
1 code implementation • 18 Apr 2024 • Urchade Zaratiana, Nadi Tomeh, Niama El Khbir, Pierre Holat, Thierry Charnois
Information extraction (IE) is an important task in Natural Language Processing (NLP), involving the extraction of named entities and their relationships from unstructured text.
Graph structure learning Joint Entity and Relation Extraction +1
no code implementations • 18 Apr 2024 • Urchade Zaratiana, Nadi Tomeh, Yann Dauxais, Pierre Holat, Thierry Charnois
Joint entity and relation extraction plays a pivotal role in various applications, notably in the construction of knowledge graphs.
1 code implementation • 2 Jan 2024 • Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois
In this paper, we propose a novel method for joint entity and relation extraction from unstructured text by framing it as a conditional sequence generation problem.
1 code implementation • 29 Nov 2023 • Urchade Zaratiana, Nadi Tomeh, Niama El Khbir, Pierre Holat, Thierry Charnois
Semi-Markov CRF has been proposed as an alternative to the traditional Linear Chain CRF for text segmentation tasks such as Named Entity Recognition (NER).
1 code implementation • 14 Nov 2023 • Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois
Named Entity Recognition (NER) is essential in various Natural Language Processing (NLP) applications.
1 code implementation • 26 Oct 2022 • Urchade Zaratiana, Niama El Khbir, Dennis Núñez, Pierre Holat, Nadi Tomeh, Thierry Charnois
Extractive question answering (ExQA) is an essential task for Natural Language Processing.
Ranked #2 on Question Answering on NaturalQA (F1 metric)
1 code implementation • 28 Mar 2022 • Urchade Zaratiana, Pierre Holat, Nadi Tomeh, Thierry Charnois
The task of Named Entity Recognition (NER) is an important component of many natural language processing systems, such as relation extraction and knowledge graph construction.
no code implementations • 21 Mar 2022 • Moussa Kamal Eddine, Nadi Tomeh, Nizar Habash, Joseph Le Roux, Michalis Vazirgiannis
Like most natural language understanding and generation tasks, state-of-the-art models for summarization are transformer-based sequence-to-sequence architectures that are pretrained on large corpora.
Abstractive Text Summarization Natural Language Understanding
no code implementations • COLING 2020 • Yash Kankanampati, Joseph Le Roux, Nadi Tomeh, Dima Taji, Nizar Habash
In this paper we present a parsing model for projective dependency trees which takes advantage of the existence of complementary dependency annotations which is the case in Arabic, with the availability of CATiB and UD treebanks.
no code implementations • JEPTALNRECITAL 2016 • Pierre Holat, Nadi Tomeh, Thierry Charnois, Delphine Battistelli, Marie-Christine Jaulent, Jean-Philippe M{\'e}tivier
Dans cet article, nous nous int{\'e}ressons {\`a} l{'}extraction d{'}entit{\'e}s m{\'e}dicales de type sympt{\^o}me dans les textes biom{\'e}dicaux.
no code implementations • JEPTALNRECITAL 2015 • Pierre Holat, Nadi Tomeh, Thierry Charnois
En classification de textes, la plupart des m{\'e}thodes fond{\'e}es sur des classifieurs statistiques utilisent des mots, ou des combinaisons de mots contigus, comme descripteurs.
no code implementations • LREC 2014 • Wajdi Zaghouani, Behrang Mohit, Nizar Habash, Ossama Obeid, Nadi Tomeh, Alla Rozovskaya, Noura Farra, Sarah Alkuhlani, Kemal Oflazer
Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations.