Name
Abstract | ||
|---|---|---|
Short-text classification is increasingly used in a wide range of applications. However, it still remains a challenging problem due to the insufficient nature of word occurrences in short-text documents, although some recently developed methods which exploit syntactic or semantic information have enhanced performance in short-text classification. The language-dependency problem, however, caused by the heavy use of grammatical tags and lexical databases, is considered the major drawback of the previous methods when they are applied to applications in diverse languages. In this article, we propose a novel kernel, called language independent semantic (LIS) kernel, which is able to effectively compute the similarity between short-text documents without using grammatical tags and lexical databases. From the experiment results on English and Korean datasets, it is shown that the LIS kernel has better performance than several existing kernels. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.eswa.2013.07.097 | Expert Syst. Appl. |
Keywords | Field | DocType |
existing kernel,short-text classification,novel kernel,lexical databases,challenging problem,lis kernel,grammatical tag,language independent semantic kernel,short-text document,better performance,language independent semantic,kernel method | Drawback,Kernel (linear algebra),Semantic similarity,Similarity measure,Computer science,Tree kernel,Exploit,Natural language processing,Artificial intelligence,Kernel method,Syntax | Journal |
Volume | Issue | ISSN |
41 | 2 | 0957-4174 |
Citations | PageRank | References |
19 | 0.71 | 27 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kwanho Kim | 1 | 361 | 37.49 |
| Beom-Suk Chung | 2 | 58 | 3.15 |
| Yerim Choi | 3 | 19 | 0.71 |
| Seungjun Lee | 4 | 21 | 6.20 |
| Jae-Yoon Jung | 5 | 297 | 31.94 |
| Jonghun Park | 6 | 491 | 37.86 |