@inproceedings{tian-etal-2016-domain,
title = "Domain Adaptation for Named Entity Recognition Using {CRF}s",
author = "Tian, Tian and
Dinarelli, Marco and
Tellier, Isabelle and
Cardoso, Pedro Dias",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1089/",
pages = "561--565",
abstract = "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."
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%0 Conference Proceedings
%T Domain Adaptation for Named Entity Recognition Using CRFs
%A Tian, Tian
%A Dinarelli, Marco
%A Tellier, Isabelle
%A Cardoso, Pedro Dias
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F tian-etal-2016-domain
%X 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.
%U https://aclanthology.org/L16-1089/
%P 561-565
Markdown (Informal)
[Domain Adaptation for Named Entity Recognition Using CRFs](https://aclanthology.org/L16-1089/) (Tian et al., LREC 2016)
ACL
- Tian Tian, Marco Dinarelli, Isabelle Tellier, and Pedro Dias Cardoso. 2016. Domain Adaptation for Named Entity Recognition Using CRFs. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 561–565, Portorož, Slovenia. European Language Resources Association (ELRA).