50 Ways to Makes Sense Of Natural Language
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Transcript

  • 1. 50 ways to make computers understandnatural language
  • 2. with 6 keytechniques
  • 3. Classifiers Bayesian ClassifiersSupport Vector Machine
  • 4. Sentence Parsing & Tagging Treat gem Ruby Linguistics Open Calais Achemy API
  • 5. Dictionaries Ruby WordNet Create your own
  • 6. Pattern MatchingRegular Expressions (also great for normalising)
  • 7. Meta DataHTML, headers, microformats, author information
  • 8. Statistical AnalysisKeyword density, worddistribution, word/time distribution
  • 9. 1Person NamesSemantic Tagging/Dictionary
  • 10. 2 Location NamesSemantic Tagging + Geocode with Yahoo API/Google Maps
  • 11. 3Important Words Dictionary, Meta Data
  • 12. 4 Email Address Pattern matching/b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,4}b/
  • 13. 5Phone Numbers Pattern matching/b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,4}b/
  • 14. 6Postal Address Pattern matchingdetect postal code or town/city and work backwards
  • 15. 7Dates & TimesDictionary, Pattern matching, Chronic Gem
  • 16. 8Company/Org Semantic Tagging
  • 17. 9 Natural PhenomenonSemantic Tagging, Dictionary
  • 18. 10Job Title Dictionary
  • 19. 11 TopicClassifier/Dictionary
  • 20. 12 QuotesPattern Matching & Semantic Tagging
  • 21. 13CurrencyPattern Matching
  • 22. 14Language Classifier
  • 23. 15 SentimentClassifier/Dictionary/Pattern Matching
  • 24. 16Potty Mouth/DHH Detector F-bomb dictionary
  • 25. 17FlamboyanceDictionary or Classifier
  • 26. 18 AgeClassifier
  • 27. 19 WealthDictionary/Classifier/Meta data
  • 28. 20HumourDictionary
  • 29. 21 Gender or Dictionary (first names)Classifier (guess from writing style)
  • 30. 22Author Location Meta Data
  • 31. 23 Person DetailsMeta Data: Wikipedia, Rapleaf
  • 32. 24Keyword Density Frequency analysis
  • 33. 25 Relationship GraphsMeta Semantic Tagging, Data(Showing connectionsbetween people mentioning each other)
  • 34. 26 Clusteringk-means clustering, hierarchical, clustering, density clustering, rsruby
  • 35. 27 Influencial PeopleSemantic Tagging, Statistical Analysis
  • 36. 28Influential LinksPattern Matching, Statistical Analysis
  • 37. 29Social Graph Meta Data
  • 38. 30Link ThumbnailsPattern Matching, Meta data
  • 39. 31Link Media TypePattern Matching, Meta Data or Dictionary
  • 40. 32Domain DetailsMeta Data (whois, alexa, ip, geocode)
  • 41. 33 Active TimeMeta Data, Statistical Analysis
  • 42. 34 Generosity(Through citation e.g. RT) Pattern Match, Semantic Tagging
  • 43. 35 Similarity Statistical Analysis: cosinesimilarity, vector space model
  • 44. 36Ethnicity Classifier
  • 45. 37Expertise Levelinfluence + topic + dictionary
  • 46. 38Tech Savviness previous or Classifier
  • 47. 39 DeviceOwnershipMeta data, dictionary
  • 48. 40Favourite BrandsSemantic Tagging or Dictionary + Sentiment Analysis + Frequency Analysis
  • 49. 41 Favourite Shopping timesSemantic Tagging or Dictionary + Sentiment Analysis + Frequency Analysis
  • 50. 42Favourite websitePattern matching + SentimentAnalysis + Frequency Analysis
  • 51. 43Organisation or Person Meta data or classifier
  • 52. 44 Happiest PlacesMeta data, geocoding, sentiment, statistical analysis
  • 53. 45 Happiest TimesMeta data, sentiment, statistical analysis
  • 54. 46Personality Type Classifier
  • 55. 47Humanality Classifier
  • 56. 48Spam Detection Classifier
  • 57. 49Product/Brand/ Film league dictionary,sentiment analysis, statistical analysis
  • 58. 50 FormalityClassifier/Dictionary
  • 59. ?