Manel Zarrouk


2020

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Un prototype en ligne pour la prédiction du niveau de compétence en anglais des productions écrites (A prototype for web-based prediction of English proficiency levels in writings)
Thomas Gaillat | Nicolas Ballier | Annanda Sousa | Manon Bouyé | Andrew Simpkin | Bernardo Stearns | Manel Zarrouk
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 4 : Démonstrations et résumés d'articles internationaux

Cet article décrit un prototype axé sur la prédiction du niveau de compétence des apprenants de l’anglais. Le système repose sur un modèle d’apprentissage supervisé, couplé à une interface web.

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From Linguistic Research Projects to Language Technology Platforms: A Case Study in Learner Data
Annanda Sousa | Nicolas Ballier | Thomas Gaillat | Bernardo Stearns | Manel Zarrouk | Andrew Simpkin | Manon Bouyé
Proceedings of the 1st International Workshop on Language Technology Platforms

This paper describes the workflow and architecture adopted by a linguistic research project. We report our experience and present the research outputs turned into resources that we wish to share with the community. We discuss the current limitations and the next steps that could be taken for the scaling and development of our research project. Allying NLP and language-centric AI, we discuss similar projects and possible ways to start collaborating towards potential platform interoperability.

2019

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CoSACT: A Collaborative Tool for Fine-Grained Sentiment Annotation and Consolidation of Text
Tobias Daudert | Manel Zarrouk | Brian Davis
Proceedings of the First Workshop on Financial Technology and Natural Language Processing

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Multilingual Multimodal Machine Translation for Dravidian Languages utilizing Phonetic Transcription
Bharathi Raja Chakravarthi | Ruba Priyadharshini | Bernardo Stearns | Arun Jayapal | Sridevy S | Mihael Arcan | Manel Zarrouk | John P McCrae
Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages

2018

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The SSIX Corpora: Three Gold Standard Corpora for Sentiment Analysis in English, Spanish and German Financial Microblogs
Thomas Gaillat | Manel Zarrouk | André Freitas | Brian Davis
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages
Siamak Barzegar | Brian Davis | Manel Zarrouk | Siegfried Handschuh | Andre Freitas
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Implicit and Explicit Aspect Extraction in Financial Microblogs
Thomas Gaillat | Bernardo Stearns | Gopal Sridhar | Ross McDermott | Manel Zarrouk | Brian Davis
Proceedings of the First Workshop on Economics and Natural Language Processing

This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.

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FinSentiA: Sentiment Analysis in English Financial Microblogs
Thomas Gaillat | Annanda Sousa | Manel Zarrouk | Brian Davis
Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN

FinSentiA: Sentiment Analysis in English Financial Microblogs The objective of this paper is to report on the building of a Sentiment Analysis (SA) system dedicated to financial microblogs in English. The purpose of our work is to build a financial classifier that predicts the sentiment of stock investors in microblog platforms such as StockTwits and Twitter. Our contribution shows that it is possible to conduct such tasks in order to provide fine grained SA of financial microblogs. We extracted financial entities with relevant contexts and assigned scores on a continuous scale by adopting a deep learning method for the classification.

2017

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SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Keith Cortis | André Freitas | Tobias Daudert | Manuela Huerlimann | Manel Zarrouk | Siegfried Handschuh | Brian Davis
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper discusses the “Fine-Grained Sentiment Analysis on Financial Microblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.

2014

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About Inferences in a Crowdsourced Lexical-Semantic Network
Mathieu Lafourcade | Manel Zarrouk | Alain Joubert
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Inferring Knowledge with Word Refinements in a Crowdsourced Lexical-Semantic Network
Manel Zarrouk | Mathieu Lafourcade
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Annotations and inference of relations in a lexical semantic network : Applied to radiology (Annotations et inférences de relations dans un réseau lexico-sémantique: application à la radiologie) [in French]
Lionel Ramadier | Manel Zarrouk | Mathieu Lafourcade | Antoine Micheau
Proceedings of TALN 2014 (Volume 1: Long Papers)

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Relation Inference in Lexical Networks ... with Refinements
Manel Zarrouk | Mathieu Lafourcade
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Improving lexical network’s quality is an important issue in the creation process of these language resources. This can be done by automatically inferring new relations from already existing ones with the purpose of (1) densifying the relations to cover the eventual lack of information and (2) detecting errors. In this paper, we devise such an approach applied to the JeuxDeMots lexical network, which is a freely available lexical and semantic resource for French. We first present the principles behind the lexical network construction with crowdsourcing and games with a purpose and illustrated them with JeuxDeMots (JDM). Then, we present the outline of an elicitation engine based on an inference engine using schemes like deduction, induction and abduction which will be referenced and briefly presented and we will especially highlight the new scheme (Relation Inference Scheme with Refinements) added to our system. An experiment showing the relevance of this scheme is then presented.

2013

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Inductive and deductive inferences in a Crowdsourced Lexical-Semantic Network
Manel Zarrouk | Mathieu Lafourcade | Alain Joubert
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Inductive and deductive inferences in a Crowdsourced Lexical-Semantic Network (Inférences déductives et réconciliation dans un réseau lexico-sémantique) [in French]
Manel Zarrouk | Mathieu Lafourcade | Alain Joubert
Proceedings of TALN 2013 (Volume 1: Long Papers)