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Synapse at CAp 2017 NER challenge: Fasttext CRF

Sileo, Damien and Pradel, Camille and Muller, Philippe and Van de Cruys, Tim Synapse at CAp 2017 NER challenge: Fasttext CRF. (2017) In: 19e Conference francophone sur l'Apprentissage Automatique (CAp 2017), 28 June 2017 - 30 June 2017 (Grenoble, France).

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Abstract

We present our system for the CAp 2017 NER challenge which is about named entity recognition on French tweets. Our system leverages unsupervised learning on a larger dataset of French tweets to learn features feeding a CRF model. It was ranked first without using any gazetteer or structured external data, with an F-measure of 58.89\%. To the best of our knowledge, it is the first system to use fasttext embeddings (which include subword representations) and an embedding-based sentence representation for NER.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-02879724
Audience (conference):National conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Synapse Développement (FRANCE)
Laboratory name:
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Deposited On:15 Jun 2020 12:27

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