ASR:2014-12-08

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2014年12月8日 (一) 02:17Lr讨论 | 贡献的版本

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Text Processing

LM development

Domain specific LM

  • domain lm
  • train some more LMs with Zhenlong (dianzishu sogou bbs chosen),put result on cvss.
  • Sougou2T : kn-count.
  • lm v2.0 continue(this week)
  • new dict.
  • Released vocab v2.0 (mainly done by Dongxu) to JieTong.


tag LM

  • summary done
  • need to do
  • tag Probability should test add the weight(hanzhenglong) and handover to hanzhenglong (hold)
  • make a summary about tag-lm and journal paper(wxx and yuanb)(this weeks).
  • Reviewed papers and begin to write paper (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 started
  • Analysis the wiki infomation of category and link into jso done, knowledge vector build graph done.
  • begin to code for train

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

deatil:

Spell mistake

  • add the xiaoI pingyin correct to framework.

improve fuzzy match

  • add Synonyms similarity using MERT-4 method(hold)

improve lucene search

  • using MERT-4 method to get good value of multi-feature.like IDF,NER,baidu_weight,keyword etc.(liurong this month)
  • now test the performance.

Multi-Scene Recognition

  • done

XiaoI framework

  • ner from xiaoI
  • new inter will install SEMPRE

patent

  • done