Name
Abstract | ||
|---|---|---|
This paper presents a method of processing Chinese syntactic category ambiguity with support vector machines (SVMs): extracting the word itself, candidate part-of-speech (POS) tags, the pair of candidate POS tags and their probability and context information as the features of the word vector. A training set is established. The machine learning models of disambiguation based on support vector machines are obtained using polynomial kernel functions. The testing results show that this method is efficient. The paper also gives the results obtained with neural networks for comparison. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11427445_39 | ISNN (2) |
Keywords | Field | DocType |
support vector machine,training set,testing result,polynomial kernel function,neural network,context information,chinese syntactic category disambiguation,candidate part-of-speech,candidate pos tag,word vector,chinese syntactic category ambiguity,part of speech,kernel function,machine learning | Pattern recognition,Computer science,Support vector machine,Model-based reasoning,Polynomial kernel,Artificial intelligence,Syntactic category,Relevance vector machine,Artificial neural network,Ambiguity,Machine learning,Kernel (statistics) | Conference |
Volume | ISSN | ISBN |
3497 | 0302-9743 | 3-540-25913-9 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lishuang Li | 1 | 109 | 19.72 |
| Lihua Li | 2 | 0 | 0.34 |
| Degen Huang | 3 | 159 | 38.71 |
| He-Ping Song | 4 | 7 | 3.32 |