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Thinking about nlp

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Pan Xiaotong
Pan Xiaotong

Description for natural language process

TechnologyEducation
1 of 26
Thinking about NLP Statistics models for NLP
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Two research branches NLU (Natural language understand)
Two research branches ,[object Object]
Technology others Part of speech tagging Semantic understand 3% 12% 9% 24% Syntactic structure analysis Segmentation 53% Which will be the most vital during a complete flow?
Rules & dictionary for NLP
Segmentation As the most necessary part for NLP, word segmentation technology experiences several years, now it has been mature relied on lots of scientists’ effort ,[object Object],[object Object],[object Object],[object Object]
Rules & dictionary Early research results, manual handling other than self-study, differentiating by splitting methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
N-Shortest path split Currently the most effective split, use Dijsktra algorithm, for higher recall rate, every time select N results and sort them by path size ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Statistics Models for NLP
Classical statistics models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
N-gram ,[object Object]
Naïve Bayes ,[object Object]
Hidden Markov Model ,[object Object]
Maximum entropy ,[object Object]
Conditional random fields ,[object Object]
Contrast between statistics models ,[object Object],[object Object],[object Object],[object Object],[object Object]
Mixed method for NLP
Mixed methods ,[object Object],[object Object],[object Object],[object Object],[object Object]
Parsing ,[object Object],[object Object],[object Object],[object Object]
Part of speech tagging ,[object Object],[object Object]
Semantic analysis ,[object Object],[object Object]
Applications
Applications ,[object Object],[object Object],[object Object],[object Object]
After today’s session Questions? For any questions not covered in this presentation , please contact me later Further e-mail queries hinsakira @ gmail .com
End Thank you

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Thinking about nlp

Editor's Notes

  1. Basic implementation of AI
  2. Corpus & statistics
  3. N-shortest split & Baidu algorithm As chinese word splitting technology
  4. N’s selection, for instance, 100 or more? About performance and time complexity analysiis
  5. Classification models, comparation CRFs with ME and HEME models
  6. N-gram model
  7. Classification model and why called it as naïve? Condition separations suppose
  8. Hidden Markov Model & Max entropy hidden markov model & conditional random fields
  9. Maximum entropy, first refers to “character” property, the post of tagging
  10. Conditional random fields, solves ME’s bias problem Remove of bias phenomenon
  11. Constract
  12. Part of speech tagging, a means for assisting splitting
  13. Related to current google’s productts
  14. 30mins – 1hours