New Approaches  at  Natural Language Processing Systems Zoltán  ANDREJKOVICS WWW.ANDZOL.COM 2008
What is  the basic problem  of NLP?
モナリザ Mona Lisa With  a little  input
The system should find out  more
There  are  4 language   area  which are problematic
   Translation I love dogs. Ich liebe die Hunden.
   Information  retrieval
   Understanding ≠ apple apple
   Searching  for  relations
How can we improve our NLP systems?
<ul><li>Review  our current NLP tools </li></ul>
   Find out why  people  are able to do more?
   Current NLP systems  Translation Understanding Realtion searching
 
A szakértők szerint ez a legnagyobb az eddig felfedezett kígyók között… According to these experts, it is the largest so f...
Kikerült az App Store-ba a Ustream   mobilalkalmazása …   Removed from the App Store into the mobile Ustream …   Ustream V...
   Doesn’t understand the sentences, not searching relations    Hard to define language with rules    Google translatio...
A word explanation system by Viktor GYENES ≠
Frog?  Horse, Toad, Monkey, Smile
Frog?
A word explanation system    Not broad functionality    No option to learn
Numenta  HTM H ierarchical  T emporal   M emory
Searching pattern Searching pattern Searching pattern Searching pattern
Letters Words Expressions Abstract expressions
Letters Words Expressions Abstract expressions Aggregation
   Hard to fit on NLP issues    Hard to find out the aggregation algorith HTM
   Human knowledge and intelligence
What are the characteristics, that  makes us a „ human ”?
Feelings
Motivation
Continual learning  (experience)
When do we learn new things?
People want to make their world  predictable
Situation Situation Expectation right wrong
If the expactation was  wrong , we want to figure out  another theory  of the world.
After the review, I found some  new principles
Knowledge depends  on the person Database replaceable not   replaceable Individual knowledge bases
Többfunkciós tudás „ analógiák” „ I bought a new  tool …” „ I bought a new  computer” a new machine, a new instrument, a n...
Our knowledge fits to the problem,  and not vica versa Right understanding
Information exchange  When we eat? Sharing thoughts
Repeation, confirmation,  exprimentation learning using knowledge learning using knowledge Continual learning
Motivation Unversal learning Synergy Proper motivation system Learning from  any  source Learning new things don’t slow th...
Next steps?
Finding new ways of storing data (knowledge)   database, ontology,  network
Find out how do we learn? statistical models,  braing , algorithm
Semantic writing <item> Cat </item> <item  rdf:type=&quot;animal&quot; > Cat </item>
System that realize what we want to do
Some good starts
FOR  YOUR  ATTENTION www.AndZol.com
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New Approaches at Natural Language Processing Systems

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  • New Approaches at Natural Language Processing Systems

    1. 1. New Approaches at Natural Language Processing Systems Zoltán ANDREJKOVICS WWW.ANDZOL.COM 2008
    2. 2. What is the basic problem of NLP?
    3. 3. モナリザ Mona Lisa With a little input
    4. 4. The system should find out more
    5. 5. There are 4 language area which are problematic
    6. 6.  Translation I love dogs. Ich liebe die Hunden.
    7. 7.  Information retrieval
    8. 8.  Understanding ≠ apple apple
    9. 9.  Searching for relations
    10. 10. How can we improve our NLP systems?
    11. 11. <ul><li>Review our current NLP tools </li></ul>
    12. 12.  Find out why people are able to do more?
    13. 13.  Current NLP systems Translation Understanding Realtion searching
    14. 15. A szakértők szerint ez a legnagyobb az eddig felfedezett kígyók között… According to these experts, it is the largest so far discovered in snakes … (hungarian news site)
    15. 16. Kikerült az App Store-ba a Ustream mobilalkalmazása … Removed from the App Store into the mobile Ustream … Ustream Viewer Added to App Store …
    16. 17.  Doesn’t understand the sentences, not searching relations  Hard to define language with rules  Google translation is stiff, language is flexible
    17. 18. A word explanation system by Viktor GYENES ≠
    18. 19. Frog? Horse, Toad, Monkey, Smile
    19. 20. Frog?
    20. 21. A word explanation system  Not broad functionality  No option to learn
    21. 22. Numenta HTM H ierarchical T emporal M emory
    22. 23. Searching pattern Searching pattern Searching pattern Searching pattern
    23. 24. Letters Words Expressions Abstract expressions
    24. 25. Letters Words Expressions Abstract expressions Aggregation
    25. 26.  Hard to fit on NLP issues  Hard to find out the aggregation algorith HTM
    26. 27.  Human knowledge and intelligence
    27. 28. What are the characteristics, that makes us a „ human ”?
    28. 29. Feelings
    29. 30. Motivation
    30. 31. Continual learning (experience)
    31. 32. When do we learn new things?
    32. 33. People want to make their world predictable
    33. 34. Situation Situation Expectation right wrong
    34. 35. If the expactation was wrong , we want to figure out another theory of the world.
    35. 36. After the review, I found some new principles
    36. 37. Knowledge depends on the person Database replaceable not replaceable Individual knowledge bases
    37. 38. Többfunkciós tudás „ analógiák” „ I bought a new tool …” „ I bought a new computer” a new machine, a new instrument, a new engine, a new camera, a new airplane etc. It means not Right understanding
    38. 39. Our knowledge fits to the problem, and not vica versa Right understanding
    39. 40. Information exchange When we eat? Sharing thoughts
    40. 41. Repeation, confirmation, exprimentation learning using knowledge learning using knowledge Continual learning
    41. 42. Motivation Unversal learning Synergy Proper motivation system Learning from any source Learning new things don’t slow the sytem
    42. 43. Next steps?
    43. 44. Finding new ways of storing data (knowledge) database, ontology, network
    44. 45. Find out how do we learn? statistical models, braing , algorithm
    45. 46. Semantic writing <item> Cat </item> <item rdf:type=&quot;animal&quot; > Cat </item>
    46. 47. System that realize what we want to do
    47. 48. Some good starts
    48. 49. FOR YOUR ATTENTION www.AndZol.com

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