Summary of the paper
| Title | Use of Unsupervised Word Classes for Entity Recognition: Application to the Detection of Disorders in Clinical Reports |
|---|---|
| Authors | Maria Evangelia Chatzimina, Cyril Grouin and Pierre Zweigenbaum |
| Abstract | Unsupervised word classes induced from unannotated text corpora are increasingly used to help tasks addressed by supervised classification, such as standard named entity detection. This paper studies the contribution of unsupervised word classes to a medical entity detection task with two specific objectives: How do unsupervised word classes compare to available knowledge-based semantic classes? Does syntactic information help produce unsupervised word classes with better properties? We design and test two syntax-based methods to produce word classes: one applies the Brown clustering algorithm to syntactic dependencies, the other collects latent categories created by a PCFG-LA parser. When added to non-semantic features, knowledge-based semantic classes gain 7.28 points of F-measure. In the same context, basic unsupervised word classes gain 4.16pt, reaching 60% of the contribution of knowledge-based semantic classes and outperforming Wikipedia, and adding PCFG-LA unsupervised word classes gain one more point at 5.11pt, reaching 70%. Unsupervised word classes could therefore provide a useful semantic back-off in domains where no knowledge-based semantic classes are available. The combination of both knowledge-based and basic unsupervised classes gains 8.33pt. Therefore, unsupervised classes are still useful even when rich knowledge-based classes exist. |
| Topics | Information Extraction, Information Retrieval |
Full paper  |
Use of Unsupervised Word Classes for Entity Recognition: Application to the Detection of Disorders in Clinical Reports |
| Bibtex | @InProceedings{CHATZIMINA14.389, author = {Maria Evangelia Chatzimina and Cyril Grouin and Pierre Zweigenbaum}, title = {Use of Unsupervised Word Classes for Entity Recognition: Application to the Detection of Disorders in Clinical Reports}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |