Name
Abstract | ||
|---|---|---|
We present an automatic semantic roles labeling system for structured trees of Chinese sentences. It adopts dependency decision making and example-based approaches. The training data and extracted examples are from the Sinica Treebank, which is a Chinese Treebank with semantic role assigned for each constituent. It used 74 abstract semantic roles including thematic roles, such as 'agent'; 'theme', 'instrument', and secondary roles of 'location', 'time', 'manner' and roles for nominal modifiers. The design of role assignment algorithm is based on the different decision features, such as head-argument/modifier, case makers, sentence structures etc. It labels semantic roles of parsed sentences. Therefore the practical performance of the system depends on a good parser which labels the right structures of sentences. The system achieves 92.71% accuracy in labeling the semantic roles for pre-structure- bracketed texts which is considerably higher than the simple method using probabilistic model of head-modifier relations. |
Year | Venue | Keywords |
|---|---|---|
2004 | SIGHAN@ACL | probabilistic model,tree structure,semantic role labeling |
Field | DocType | Citations |
Training set,Information retrieval,Computer science,Statistical model,Natural language processing,Tree structure,Artificial intelligence,Treebank,Parsing,Sentence,Semantic role labeling | Conference | 15 |
PageRank | References | Authors |
1.23 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia-Ming You | 1 | 28 | 3.35 |
| Keh-Jiann Chen | 2 | 761 | 131.86 |