Name
Abstract | ||
|---|---|---|
It is difficult to cope with data sparseness, unless augmenting the size of the dictionary in a stochastic-based word-spacing model is an option. To resolve both data sparseness and the dictionary memory size problem, this paper describes the process of dynamically providing candidate words to detect correct words using morpheme unigrams and their categories. Each candidate word's probability was estimated from the morpheme probability, which was weighted according to its category. The category weights were trained to minimize the mean of the errors between the observed probability of a word and that estimated by the word's individual morpheme probability weighted by its category power in a category pattern for producing the given word. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11940098_30 | ICCPOL |
Keywords | Field | DocType |
morpheme unigrams,category power,morpheme probability,individual morpheme probability,data sparseness,candidate word,observed probability,category pattern,correct word,category-pattern-based korean word-spacing,category weight | Morpheme,Computer science,Mean squared error,Speech recognition,Natural language processing,Artificial intelligence,Probability of error | Conference |
Volume | ISSN | ISBN |
4285 | 0302-9743 | 3-540-49667-X |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mi-Young Kang | 1 | 40 | 11.87 |
| Sungwon Jung | 2 | 320 | 59.65 |
| Hyuk-Chul Kwon | 3 | 136 | 29.02 |