Pedro Dias Cardoso

Also published as: Pedro Dias Cardoso


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Sentiment Lexicon Creation using Continuous Latent Space and Neural Networks
Pedro Dias Cardoso | Anindya Roy
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Domain Adaptation for Named Entity Recognition Using CRFs
Tian Tian | Marco Dinarelli | Isabelle Tellier | Pedro Dias Cardoso
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper we explain how we created a labelled corpus in English for a Named Entity Recognition (NER) task from multi-source and multi-domain data, for an industrial partner. We explain the specificities of this corpus with examples and describe some baseline experiments. We present some results of domain adaptation on this corpus using a labelled Twitter corpus (Ritter et al., 2011). We tested a semi-supervised method from (Garcia-Fernandez et al., 2014) combined with a supervised domain adaptation approach proposed in (Raymond and Fayolle, 2010) for machine learning experiments with CRFs (Conditional Random Fields). We use the same technique to improve the NER results on the Twitter corpus (Ritter et al., 2011). Our contributions thus consist in an industrial corpus creation and NER performance improvements.