Salah Ait-Mokhtar

Also published as: Salah Aït-Mokhtar


2021

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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.

2019

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“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

2013

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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)

2001

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

1997

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

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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