Paper Info
Title
Domain Adaptation for Named Entity Recognition Using CRFs.
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.
Year
Venue
Keywords
2016
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
Domain Adaptation,Social Media,CRFs,Machine Learning
Field
DocType
Citations 
Domain adaptation,Computer science,Speech recognition,Natural language processing,Artificial intelligence,Named-entity recognition,CRFS
Conference
0
PageRank 
References 
Authors
0.34
13
4
Name
Order
Citations
PageRank
Tian Tian112017.90
Marco Dinarelli27911.21
isabelle tellier38420.31
Pedro Miguel Dias Cardoso410.75