Differences
This shows you the differences between two versions of the page.
| Both sides previous revision
Previous revision
|
Next revision
Both sides next revision
|
courses:rg:2012:encouraging-consistent-translation-bushra [2012/10/23 15:33]
jawaid |
courses:rg:2012:encouraging-consistent-translation-bushra [2012/10/23 15:33]
jawaid |
| - BM25 which is used as term weighting function is a well known ranking function in the filed of information retrieval and a refined version of TF-IDF (another ranking function uses in IR). | - BM25 which is used as term weighting function is a well known ranking function in the filed of information retrieval and a refined version of TF-IDF (another ranking function uses in IR). |
| - C<sub>1</sub> is a fine-grain approach of term weighting function and it is computed by counting how many times rule was applied in first-pass. This approach suffers when source and target phrase differs only in non-terminal positioning or use of determiners. | - C<sub>1</sub> is a fine-grain approach of term weighting function and it is computed by counting how many times rule was applied in first-pass. This approach suffers when source and target phrase differs only in non-terminal positioning or use of determiners. |
| - C<sub>2</sub> on other hand is a coarse-grain function which takes only target tokens into account. To us, C2 looks similar to the language model feature but trained only on the target side of the dev set. | - C<sub>2</sub> on other hand is a coarse-grain function which takes only target tokens into account. To us, C<sub>2</sub> looks similar to the language model feature but trained only on the target side of the dev set. |
| - C<sub>3</sub> goes over all alignment pairs and for each rule it select those term pairs that have maximum feature vaue. | - C<sub>3</sub> goes over all alignment pairs and for each rule it select those term pairs that have maximum feature vaue. |
| |
