Ismail El Maarouf

Also published as: Ismail El Maarouf, Ismaïl El Maarouf


2021

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FinSim-3: The 3rd Shared Task on Learning Semantic Similarities for the Financial Domain
Juyeon Kang | Ismail El Maarouf | Sandra Bellato | Mei Gan
Proceedings of the Third Workshop on Financial Technology and Natural Language Processing

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The Financial Document Structure Extraction Shared Task (FinTOC2021)
Ismail El Maarouf | Juyeon Kang | Abderrahim Ait Azzi | Sandra Bellato | Mei Gan | Mahmoud El-Haj
Proceedings of the 3rd Financial Narrative Processing Workshop

2020

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The Financial Document Structure Extraction Shared task (FinToc 2020)
Najah-Imane Bentabet | Rémi Juge | Ismail El Maarouf | Virginie Mouilleron | Dialekti Valsamou-Stanislawski | Mahmoud El-Haj
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

This paper presents the FinTOC-2020 Shared Task on structure extraction from financial documents, its participants results and their findings. This shared task was organized as part of The 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING’2020). This shared task aimed to stimulate research in systems for extracting table-of-contents (TOC) from investment documents (such as financial prospectuses) by detecting the document titles and organizing them hierarchically into a TOC. For the second edition of this shared task, two subtasks were presented to the participants: one with English documents and the other one with French documents.

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The FinSim 2020 Shared Task: Learning Semantic Representations for the Financial Domain
Ismail El Maarouf | Youness Mansar | Virginie Mouilleron | Dialekti Valsamou-Stanislawski
Proceedings of the Second Workshop on Financial Technology and Natural Language Processing

2015

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SemEval-2015 Task 15: A CPA dictionary-entry-building task
Vít Baisa | Jane Bradbury | Silvie Cinková | Ismaïl El Maarouf | Adam Kilgarriff | Octavian Popescu
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Barbecued Opakapaka: Using Semantic Preferences for Ontology Population
Ismail El Maarouf | Georgiana Marsic | Constantin Orăsan
Proceedings of the International Conference Recent Advances in Natural Language Processing

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The GuanXi network: a new multilingual LLOD for Language Learning applications
Ismail El Maarouf | Hatem Mousselly-Sergieh | Eugene Alferov | Haofen Wang | Zhijia Fang | Doug Cooper
Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data

2014

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UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual Entailment
Rohit Gupta | Hanna Béchara | Ismail El Maarouf | Constantin Orăsan
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Disambiguating Verbs by Collocation: Corpus Lexicography meets Natural Language Processing
Ismail El Maarouf | Jane Bradbury | Vít Baisa | Patrick Hanks
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper reports the results of Natural Language Processing (NLP) experiments in semantic parsing, based on a new semantic resource, the Pattern Dictionary of English Verbs (PDEV) (Hanks, 2013). This work is set in the DVC (Disambiguating Verbs by Collocation) project , a project in Corpus Lexicography aimed at expanding PDEV to a large scale. This project springs from a long-term collaboration of lexicographers with computer scientists which has given rise to the design and maintenance of specific, adapted, and user-friendly editing and exploration tools. Particular attention is drawn on the use of NLP deep semantic methods to help in data processing. Possible contributions of NLP include pattern disambiguation, the focus of this article. The present article explains how PDEV differs from other lexical resources and describes its structure in detail. It also presents new classification experiments on a subset of 25 verbs. The SVM model obtained a micro-average F1 score of 0.81.

2013

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An empirical classification of verbs based on Semantic Types: the case of the ‘poison’ verbs.
Jane Bradbury | Ismail El Maarouf
Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora

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Automatic classification of semantic patterns from the Pattern Dictionary of English Verbs
Ismaïl El Maarouf | Vít Baisa
Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora

2012

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A French Fairy Tale Corpus syntactically and semantically annotated
Ismaïl El Maarouf | Jeanne Villaneau
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Fairy tales, folktales and more generally children stories have lately attracted the Natural Language Processing (NLP) community. As such, very few corpora exist and linguistic resources are lacking. The work presented in this paper aims at filling this gap by presenting a syntactically and semantically annotated corpus. It focuses on the linguistic analysis of a Fairy Tales Corpus, and provides the description of the syntactic and semantic resources developed for Information Extraction. Resources include syntactic dependency relation annotation for 120 verbs; referential annotation, which is concerned with annotating each anaphoric occurrence and Proper Name with the most specific noun in the text; ontology matching for a substantial part of the nouns in the corpus; semantic role labelling for 41 verbs using the FrameNet database. The article also sums up previous analyses of this corpus and indicates possible uses of this corpus for the NLP community.

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Parenthetical Classification for Information Extraction
Ismail El Maarouf | Jeanne Villaneau
Proceedings of COLING 2012: Posters

2011

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Extraction de patrons sémantiques appliquée à la classification d’Entités Nommées (Extraction of semantic patterns applied to the classification of named entities)
Ismaïl El Maarouf | Jeanne Villaneau | Sophie Rosset
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

La variabilité des corpus constitue un problème majeur pour les systèmes de reconnaissance d’entités nommées. L’une des pistes possibles pour y remédier est l’utilisation d’approches linguistiques pour les adapter à de nouveaux contextes : la construction de patrons sémantiques peut permettre de désambiguïser les entités nommées en structurant leur environnement syntaxico-sémantique. Cet article présente une première réalisation sur un corpus de presse d’un système de correction. Après une étape de segmentation sur des critères discursifs de surface, le système extrait et pondère les patrons liés à une classe d’entité nommée fournie par un analyseur. Malgré des modèles encore relativement élémentaires, les résultats obtenus sont encourageants et montrent la nécessité d’un traitement plus approfondi de la classe Organisation.