NP Chunking (State of the art)
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- Performance measure: F = 2 * Precision * Recall / (Recall + Precision)
- Precision: percentage of NPs found by the algorithm that are correct
- Recall: percentage of NPs defined in the corpus that were found by the chunking program
- Training data: sections 15-18 of Wall Street Journal corpus (Ramshaw and Marcus)
- Testing data: section 20 of Wall Street Journal corpus (Ramshaw and Marcus)
- original data of the NP chunking experiments by Lance Ramshaw and Mitch Marcus
- data contains one word per line and each line contains six fields of which only the first three fields are relevant: the word, the part-of-speech tag assigned by the Brill tagger, and the correct IOB tag
- dataset is available from ftp://ftp.cis.upenn.edu/pub/chunker/
- more information is available from NP Chunking
| System name | Short description | Main publications | Software | Results (F) |
|---|---|---|---|---|
| KM00 | B-I-O tagging using SVM classifiers with polynomial kernel | Kudo and Matsumoto (2000) | YAMCHA Toolkit (but models are not provided) | 93.79% |
| KM01 | learning as in KM00, but voting between different representations | Kudo and Matsumoto (2001) | No | 94.22% |
| SS05 | specialized HMM + voting between different representations | Shen and Sarkar (2005) | No | 95.23% |
Kudo, T., and Matsumoto, Y. (2000). Use of support vector learning for chunk identification. Proceedings of the 4th Conference on CoNLL-2000 and LLL-2000, pages 142-144, Lisbon, Portugal.
Kudo, T., and Matsumoto, Y. (2001). Chunking with support vector machines. Proceedings of NAACL-2001.
Shen, H., and Sarkar, A. (2005). Voting between multiple data representations for text chunking. Proceedings of the Eighteenth Meeting of the Canadian Society for Computational Intelligence, Canadian AI 2005.