Summary of the paper
| Title | Improving Domain-specific Entity Recognition with Automatic Term Recognition and Feature Extraction |
|---|---|
| Authors | Ziqi Zhang, José Iria and Fabio Ciravegna |
| Abstract | Domain specific entity recognition often relies on domain-specific knowledge to improve system performance. However, such knowledge often suffers from limited domain portability and is expensive to build and maintain. Therefore, obtaining it in a generic and unsupervised manner would be a desirable feature for domain-specific entity recognition systems. In this paper, we introduce an approach that exploits domain-specificity of words as a form of domain-knowledge for entity-recognition tasks. Compared to prior work in the field, our approach is generic and completely unsupervised. We empirically show an improvement in entity extraction accuracy when features derived by our unsupervised method are used, with respect to baseline methods that do not employ domain knowledge. We also compared the results against those of existing systems that use manually crafted domain knowledge, and found them to be competitive. |
| Topics | Named Entity recognition, Information Extraction, Information Retrieval, Statistical and machine learning methods |
Full paper |
Improving Domain-specific Entity Recognition with Automatic Term Recognition and Feature Extraction |
Slides |
- |
| Bibtex | @InProceedings{ZHANG10.214, author = {Ziqi Zhang and José Iria and Fabio Ciravegna}, title = {Improving Domain-specific Entity Recognition with Automatic Term Recognition and Feature Extraction}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |