UniPi: Recognition of Mentions of Disorders in Clinical Text

@inproceedings{Attardi2014UniPiRO,
  title={UniPi: Recognition of Mentions of Disorders in Clinical Text},
  author={Giuseppe Attardi and Vittoria Cozza and Daniele Sartiano},
  booktitle={SemEval@COLING},
  year={2014}
}
The paper describes our experiments addressing the SemEval 2014 task on the Analysis of Clinical text. Our approach consists in extending the techniques of NE recognition, based on sequence labelling, to address the special issues of this task, i.e. the presence of overlapping and discontiguous mentions and the requirement to map the mentions to unique identifiers. We explored using supervised methods in combination with word embeddings generated from unannotated data. 

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References

Publications referenced by this paper.
Showing 1-10 of 16 references

Diff, Match and Patch libraries for Plain Text

Neil Fraser.
(Based on Myer's diff algorithm). • 2011

Natural Language Processing (almost) from Scratch

Journal of Machine Learning Research • 2011
View 2 Excerpts

Tanl (Text Analytics and Natural Language Processing). SemaWiki project: http://medialab.di.unipi.it/wiki/SemaWiki

Giuseppe Attardi
2009

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