3,625 research outputs found
Fine-grained Dutch named entity recognition
This paper describes the creation of a fine-grained named entity annotation scheme and corpus for Dutch, and experiments on automatic main type and subtype named entity recognition. We give an overview of existing named entity annotation schemes, and motivate our own, which describes six main types (persons, organizations, locations, products, events and miscellaneous named entities) and finer-grained information on subtypes and metonymic usage. This was applied to a one-million-word subset of the Dutch SoNaR reference corpus. The classifier for main type named entities achieves a micro-averaged F-score of 84.91 %, and is publicly available, along with the corpus and annotations
Synapse at CAp 2017 NER challenge: Fasttext CRF
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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