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courses:rg:2012:longdtreport [2012/03/12 22:49] longdt |
courses:rg:2012:longdtreport [2012/03/12 22:59] (current) longdt |
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__Sorted Array__ | __Sorted Array__ | ||
- | + Use n array for n-gram model (array i-th is used for i-gram) | + | |
- | | + | - Use n array for n-gram model (array i-th is used for i-gram) |
- | | + | - Each element in array in pair (w,c) |
- | | + | |
- | | + | |
+ | - Sort base on w | ||
Improvement : Implicitly encode W (all n-gram ending with particular word wi are stored -> wasteful. So, maintain another array save the beginning and the end of the range | Improvement : Implicitly encode W (all n-gram ending with particular word wi are stored -> wasteful. So, maintain another array save the beginning and the end of the range | ||
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the underline reason is only to support binary search | the underline reason is only to support binary search | ||
- | ==== Conclusion | + | ==== Decoding |
+ | **I. Exploiting Repetitive Queries** | ||
- | All in all, it was a paper worth reading, well presented, and thoroughly | + | The method use cache to speed up the process |
- | discussed, bringing useful general ideas as well as interesting details. | + | This simple implementation increase performance of 300% over conventional implementation |
+ | **II. Exploiting Scrolling Queries** | ||
+ | |||
+ | We can quickly form the context encoding of the next query by concatenating new words with saved offset from previous query | ||
+ | |||
+ | ==== Conclusion ==== | ||
+ | In summary, it was a worth reading | ||
+ | discussed. |