Salah Ait-Mokhtar

Also published as: Salah Ait Mokhtar, Salah Aït-Mokhtar


Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training
Amir Soleimani | Vassilina Nikoulina | Benoit Favre | Salah Ait Mokhtar
Proceedings of the 21st Workshop on Biomedical Language Processing

We study the zero-shot setting for the aspect-based scientific document summarization task. Summarizing scientific documents with respect to an aspect can remarkably improve document assistance systems and readers experience. However, existing large-scale datasets contain a limited variety of aspects, causing summarization models to over-fit to a small set of aspects and a specific domain. We establish baseline results in zero-shot performance (over unseen aspects and the presence of domain shift), paraphrasing, leave-one-out, and limited supervised samples experimental setups. We propose a self-supervised pre-training approach to enhance the zero-shot performance. We leverage the PubMed structured abstracts to create a biomedical aspect-based summarization dataset. Experimental results on the PubMed and FacetSum aspect-based datasets show promising performance when the model is pre-trained using unlabelled in-domain data.


Semantic Context Path Labeling for Semantic Exploration of User Reviews
Salah Aït-Mokhtar | Caroline Brun | Yves Hoppenot | Agnes Sandor
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In this paper we present a prototype demonstrator showcasing a novel method to perform semantic exploration of user reviews. The system enables effective navigation in a rich contextual semantic schema with a large number of structured classes indicating relevant information. In order to identify instances of the structured classes in the reviews, we defined a new Information Extraction task called Semantic Context Path (SCP) labeling, which simultaneously assigns types and semantic roles to entity mentions. Reviews can rapidly be explored based on the fine-grained and structured semantic classes. As a proof-of-concept, we have implemented this system for reviews on Points-of-Interest, in English and Korean.


“Sentiment Aware Map” : exploration cartographique de points d’intérêt via l’analyse de sentiments au niveau des aspects ()
Ioan Calapodescu | Caroline Brun | Vassilina Nikoulina | Salah Aït-Mokhtar
Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume IV : Démonstrations


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A Framework to Generate Sets of Terms from Large Scale Medical Vocabularies for Natural Language Processing
Salah Aït-Mokhtar | Caroline Hagège | Pajolma Rupi
Proceedings of the IWCS 2013 Workshop on Computational Semantics in Clinical Text (CSCT 2013)


A Multi-Input Dependency Parser
Salah Aït-Mokhtar | Jean-Pierre Chanod | Claude Roux
Proceedings of the Seventh International Workshop on Parsing Technologies


Subject and Object Dependency Extraction Using Finite-State Transducers
Salah Ait-Mokhtar | Jean-Pierre Chanod
Automatic Information Extraction and Building of Lexical Semantic Resources for NLP Applications

Incremental Finite-State Parsing
Salah Ait-Mokhtar | Jean-Pierre Chanod
Fifth Conference on Applied Natural Language Processing