NP Chunking (State of the art)

• 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 http://ifarm.nl/erikt/research/np-chunking.html

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

KM00 - Taku Kudo and Yuji Matsumoto. 2000b. Use of Support Vector Learning for Chunk Identification. In Proceedings of the 4th Conference on CoNLL-2000 and LLL-2000 [1]

KM01 - Taku Kudo and Yuji Matsumoto. Chunking with support vector machines. In NAACL-2001 [2]

Sarkar2005 - Hong Shen and Anoop Sarkar. Voting between Multiple Data Representations for Text Chunking. In proceedings of the Eighteenth Meeting of the Canadian Society for Computational Intelligence, Canadian AI 2005. [3]