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arXiv · 1709.04820

Synapse at CAp 2017 NER challenge: Fasttext CRF

Damien Sileo·Camille Pradel·Philippe Muller·Tim Van de Cruys
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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.

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Damien Sileo, Camille Pradel, Philippe Muller, Tim Van de Cruys. 2017-09-14. Synapse at CAp 2017 NER challenge: Fasttext CRF. https://arxiv.org/abs/1709.04820

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