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50 Ways to Makes Sense Of Natural Language
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50 Ways to Makes Sense Of Natural Language

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50 Ways to Makes Sense Of Natural Language 50 Ways to Makes Sense Of Natural Language Presentation Transcript

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