“ASR:2014-12-29”版本间的差异
来自cslt Wiki
(→Domain specific LM) |
(→improve lucene search) |
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第54行: | 第54行: | ||
====improve lucene search==== | ====improve lucene search==== | ||
:* add more feature to improve search. | :* add more feature to improve search. | ||
− | ::* | + | ::* POS, NER ,tf ,idf .. |
+ | |||
====XiaoI framework==== | ====XiaoI framework==== | ||
* context in xiaoI | * context in xiaoI |
2014年12月29日 (一) 06:23的版本
Text Processing
LM development
Domain specific LM
- LM2.0
- data check for lexicon(jietong)
- merge lm with NAME POI etc.(hanzhenglong/wxx)
- mix the sougou2T-lm,kn-discount continue
- train a large lm using 25w-dict.(hanzhenglong/wxx)
- prun history lm(wxx)
- new dict.
- dongxu help zhenglong with large dictionary.
tag LM
- need to do
- tag Probability should test add the weight(hanzhenglong) and handover to hanzhenglong (hold)
- paper
- modify the paper(yuanb two days),paper submit this week.
RNN LM
- rnn
- test wer RNNLM on Chinese data from jietong-data(this week)
- generate the ngram model from rnnlm and test the ppl with different size txt.[1]
- lstm+rnn
- check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)
Word2Vector
W2V based doc classification
- Initial results variable Bayesian GMM obtained. Performance is not as good as the conventional GMM.(hold)
- Non-linear inter-language transform: English-Spanish-Czch: wv model training done, transform model on investigation
Knowledge vector
- Knowledge vector
- Make a proper test set.
- Modify the object function and training process.
- Read Liu's paper.
relation
- Accomplish transE with almost the same performance as the paper did(even better)[2]
Character to word
- Character to word conversion(hold)
- prepare the task: word similarity
- prepare the dict.
Translation
- v5.0 demo released
- cut the dict and use new segment-tool
QA
improve fuzzy match
- add Synonyms similarity using MERT-4 method(hold)
improve lucene search
- add more feature to improve search.
- POS, NER ,tf ,idf ..
XiaoI framework
- context in xiaoI
query normalization
- using NER to normalize the word
- new inter will install SEMPRE