汉语框架语义角色的自动标注.doc

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汉语框架语义角色的自动标注
李济洪 1+, 王瑞波 1, 王蔚林 2, 李国臣 3
1(山西大学计算中心,山西太原 030006)
2(山西大学数学科学学院,山西太原 030006)
3(山西大学计算机与信息技术学院,山西太原 030006)
Automatic Labeling of Semantic Roles on Chinese
LI Ji-Hong1+, WANG Rui-Bo1, WANG Wei-Lin2, LI Guo-Chen3
puter Center, Shanxi University, Taiyuan 030006, China)
2(School of Mathematical Sciences, Shanxi University, Taiyuan 030006, China)
3(School puter and Information Technology, Shanxi University, Taiyuan 030006, China)
+ Corresponding author: E-mail: ******@sxu.
Li JH, Wang RB, Wang WL, Li GC. Automatic labeling of semantic roles on Chinese . Journal of
Software, 2010,21(4):597−611. /1000-9825/
Abstract: Based on the semantic knowledge base of Chinese (CFN) self-developed by Shanxi University, automatic labeling of the semantic roles of Chinese is turned into a sequential tagging problem at word-level by applying IOB (inside/outside/begin) strategies to the exemplified sentences in CFN corpus, and the Conditional Random Fields (CRF) model is adopted. The basic unit of tagging is word. The word, its part of speech, its relative position to the target word, the target word, and bination are chosen as the features. Various model templates are formed through optional size windows in each feature, and the orthogonal array within statistics is employed for screening of the better template. All experiments are based on the6 692 exemplified sentences of 25 frames selected from CFN corpus. The separate model is trained for each frame on its exemplified sentences by 2-fold cross-validation, and the processing of identification and classification for the semantic roles are taken simultaneously. Finally, with the target word given in a sentence, as well as the frame name of the target word, the experimental results on all 25 frames data for the precision, the recall, and F1-measure are %,
%, %, respectively.
Key words:
Chinese ; semantic role labeling; orthogonal array; feature selection; conditional random
fields