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UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations

Suwisa Kaewphan, Kai Hakala, Filip Ginter, UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations. In: Preslav Nakov, Torsten Zesch (Eds.), Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) ,, 807–811, Association for Computational Linguistics, 2014.

Abstract:

In this paper we present our system participating in the SemEval-2014 Task 7 in both subtasks A and B, aiming at recognizing and normalizing disease and symptom mentions from electronic medical records respectively. In subtask A, we used an existing NER system, NERsuite, with our own feature set tailored for this task. For subtask B, we combined word vector representations and supervised machine learning to map the recognized mentions to the corresponding UMLS concepts. Our system was placed 2nd and 5th out of 21 participants on subtasks A and B respectively showing competitive performance.

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BibTeX entry:

@INPROCEEDINGS{inpKaHaGi14a,
  title = {UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations},
  booktitle = {Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) ,},
  author = {Kaewphan, Suwisa and Hakala, Kai and Ginter, Filip},
  editor = {Nakov, Preslav and Zesch, Torsten},
  publisher = {Association for Computational Linguistics},
  pages = {807–811},
  year = {2014},
}

Belongs to TUCS Research Unit(s): Biomathematics Research Unit (BIOMATH)

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