Mohamed Ben Jannet


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Using Contextual Sentence Analysis Models to Recognize ESG Concepts
Elvys Linhares Pontes | Mohamed Ben Jannet | Jose G. Moreno | Antoine Doucet
Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)

This paper summarizes the joint participation of the Trading Central Labs and the L3i laboratory of the University of La Rochelle on both sub-tasks of the Shared Task FinSim-4 evaluation campaign. The first sub-task aims to enrich the ‘Fortia ESG taxonomy’ with new lexicon entries while the second one aims to classify sentences to either ‘sustainable’ or ‘unsustainable’ with respect to ESG (Environment, Social and Governance) related factors. For the first sub-task, we proposed a model based on pre-trained Sentence-BERT models to project sentences and concepts in a common space in order to better represent ESG concepts. The official task results show that our system yields a significant performance improvement compared to the baseline and outperforms all other submissions on the first sub-task. For the second sub-task, we combine the RoBERTa model with a feed-forward multi-layer perceptron in order to extract the context of sentences and classify them. Our model achieved high accuracy scores (over 92%) and was ranked among the top 5 systems.


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ETER : a new metric for the evaluation of hierarchical named entity recognition
Mohamed Ben Jannet | Martine Adda-Decker | Olivier Galibert | Juliette Kahn | Sophie Rosset
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper addresses the question of hierarchical named entity evaluation. In particular, we focus on metrics to deal with complex named entity structures as those introduced within the QUAERO project. The intended goal is to propose a smart way of evaluating partially correctly detected complex entities, beyond the scope of traditional metrics. None of the existing metrics are fully adequate to evaluate the proposed QUAERO task involving entity detection, classification and decomposition. We are discussing the strong and weak points of the existing metrics. We then introduce a new metric, the Entity Tree Error Rate (ETER), to evaluate hierarchical and structured named entity detection, classification and decomposition. The ETER metric builds upon the commonly accepted SER metric, but it takes the complex entity structure into account by measuring errors not only at the slot (or complex entity) level but also at a basic (atomic) entity level. We are comparing our new metric to the standard one using first some examples and then a set of real data selected from the ETAPE evaluation results.


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Automatic Named Entity Pre-annotation for Out-of-domain Human Annotation
Sophie Rosset | Cyril Grouin | Thomas Lavergne | Mohamed Ben Jannet | Jérémy Leixa | Olivier Galibert | Pierre Zweigenbaum
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse