TY - GEN
T1 - Automatic identifying of maximal length noun phrase
AU - Li, Yegang
AU - Huang, Heyan
PY - 2013/11/13
Y1 - 2013/11/13
N2 - The automatic recognition of the maximal-length noun phrase (MNP) helps to the shallow parsing. In this paper, automatic labeling of Chinese MNP is regarded as a sequential labeling task and Support Vector Machine model (SVM) is employed in the model. We propose a method which takes 2-phase hybrid approach which first identifies base chunk and then identifies MNP. Furthermore, the base chunk features can be exploited to improve performance of MNP recognition. In addition, both left-right and right-left sequential labeling were employed to identify Chinese MNP by bidirectional sequence labeling merging. The data set in the experiments is selected from Penn Chinese Treebank 5.0 Corpus, and split into train set, development set and test set according to the proportion of 4:4:1. Experimental result shows a high quality performance of 90.13% in F1-measure.
AB - The automatic recognition of the maximal-length noun phrase (MNP) helps to the shallow parsing. In this paper, automatic labeling of Chinese MNP is regarded as a sequential labeling task and Support Vector Machine model (SVM) is employed in the model. We propose a method which takes 2-phase hybrid approach which first identifies base chunk and then identifies MNP. Furthermore, the base chunk features can be exploited to improve performance of MNP recognition. In addition, both left-right and right-left sequential labeling were employed to identify Chinese MNP by bidirectional sequence labeling merging. The data set in the experiments is selected from Penn Chinese Treebank 5.0 Corpus, and split into train set, development set and test set according to the proportion of 4:4:1. Experimental result shows a high quality performance of 90.13% in F1-measure.
KW - 2-phase
KW - MNP
KW - base chunk feature
KW - bidirectional sequence labeling merging
UR - http://www.scopus.com/inward/record.url?scp=84890337540&partnerID=8YFLogxK
U2 - 10.1109/CCIS.2012.6664624
DO - 10.1109/CCIS.2012.6664624
M3 - Conference contribution
AN - SCOPUS:84890337540
SN - 9781467318556
T3 - Proceedings - 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, IEEE CCIS 2012
SP - 1445
EP - 1448
BT - Proceedings - 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, IEEE CCIS 2012
T2 - 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems, IEEE CCIS 2012
Y2 - 30 October 2012 through 1 November 2012
ER -