Name
Abstract | ||
|---|---|---|
In this paper, we proposed an efficient and accurate text chunking system using linear SVM kernel and a new technique called masked method. Previous researches indicated that systems combination or external parsers can enhance the chunking performance. However, the cost of constructing multi-classifiers is even higher than developing a single processor. Moreover, the use of external resources will complicate the original tagging process. To remedy these problems, we employ richer features and propose a masked-based method to solve unknown word problem to enhance system performance. In this way, no external resources or complex heuristics are required for the chunking system. The experiments show that when training with the CoNLL-2000 chunking dataset, our system achieves 94.12 in F"("@b") rate with linear. Furthermore, our chunker is quite efficient since it adopts a linear kernel SVM. The turn-around tagging time on CoNLL-2000 testing data is less than 50s which is about 115 times than polynomial kernel SVM. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.knosys.2006.04.016 | Knowl.-Based Syst. |
Keywords | Field | DocType |
masked method,chunking system,external parsers,chunking performance,support vector machines,linear kernel,polynomial kernel,efficient text,shallow parsing,conll-2000 testing data,text chunking,conll-2000 chunking dataset,system performance,external resource,linear svm kernel,word problem,support vector machine | Shallow parsing,Kernel (linear algebra),Pattern recognition,Computer science,Support vector machine,Heuristics,Polynomial kernel,Test data,Chunking (psychology),Artificial intelligence,Parsing,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 3 | Knowledge-Based Systems |
Citations | PageRank | References |
6 | 0.42 | 29 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Chieh Wu | 1 | 247 | 23.16 |
| Chia-Hui Chang | 2 | 1062 | 64.41 |