Summary of the paper
| Title | mwetoolkit: a Framework for Multiword Expression Identification |
|---|---|
| Authors | Carlos Ramisch, Aline Villavicencio and Christian Boitet |
| Abstract | This paper presents the Multiword Expression Toolkit (mwetoolkit), an environment for type and language-independent MWE identification from corpora. The mwetoolkit provides a targeted list of MWE candidates, extracted and filtered according to a number of user-defined criteria and a set of standard statistical association measures. For generating corpus counts, the toolkit provides both a corpus indexation facility and a tool for integration with web search engines, while for evaluation, it provides validation and annotation facilities. The mwetoolkit also allows easy integration with a machine learning tool for the creation and application of supervised MWE extraction models if annotated data is available. In our experiment, the mwetoolkit was tested and evaluated in the context of MWE extraction in the biomedical domain. Our preliminary results show that the toolkit performs better than other approaches, especially concerning recall. Moreover, this first version can also be extended in several ways in order to improve the quality of the results. |
| Topics | MultiWord Expressions & Collocations, Acquisition, Statistical and machine learning methods |
Full paper |
mwetoolkit: a Framework for Multiword Expression Identification |
Slides |
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| Bibtex | @InProceedings{RAMISCH10.803, author = {Carlos Ramisch and Aline Villavicencio and Christian Boitet}, title = {mwetoolkit: a Framework for Multiword Expression Identification}, 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} } |