“Hulan-2014-11-06”版本间的差异
来自cslt Wiki
（→improve lucene search） |
（→Spell mistake） |
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| 第2行： | 第2行： | ||
==Algorithm== | ==Algorithm== | ||
===Spell mistake=== | ===Spell mistake=== | ||
| − | :* retrain the ngram model | + | :* retrain the ngram model(caoli) |
| + | |||
===improve lucene search=== | ===improve lucene search=== | ||
* our vsm method | * our vsm method | ||
2014年11月6日 (四) 08:46的版本
目录
Dialog system
Algorithm
Spell mistake
- retrain the ngram model(caoli)
improve lucene search
- our vsm method
| method | lucene | vsm_idf(haiguan) | VSM_idf(baidu) | vsm_idf(tain) | vsm_idf(calculate) |
|---|---|---|---|---|---|
| Accary | 0.6628 | 0.6228 | 0.6197 | 0.5827 | 0.5426 |
- lucene top
- top10(82.95%),top20(86.34),top50(90.23%),top100(94.11%),top200(96.18%),top1000(97.31%),top2000(97.87%),top5000(98.75%),top10000(99.06)
- lucene Optimization(liurong)
- rewrite the method to select the 50 standard question not same template.
- check the word segment for template.
- boost the query keyword using IDF
| method | Default | idf_train | idf_train_norm | idf_baidu | idf_baidu_norm |
|---|---|---|---|---|---|
| Accary | 0.66228 | 0.651629 | 0.57644 | 0.647869 | 0.65288 |
- using MERT-4 method to get good value of multi-feature.like IDF,NER,baidu_weight,keyword etc.
Multi-Scene Recognition
- add the triples search to QA engine
- discuss the detail and give a report.
knowledge structure
- structure the default answer using attributes of the entity.
Knowledge Management and labeling system
- prepare the interface and function.
plan to do
plan to discuss
- add the triples search to QA engine
