SwiftRiver 2011 Overview


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A look at the SwiftRiver platform, the progress of it's various APIs and apps.

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SwiftRiver 2011 Overview

  1. 1. SWIFT RIVER 2011 Jon Gosier, Director of Product http://swiftly.org @swiftriver @jongos An Ushahidi Initiative
  2. 2. Initial development began during the Haiti earthquakes,one of Ushahidi’s largest deployments to date.Objective became to offer smart tools for curating real-time data of all types (Email, Twitter, SMS, Web feeds).
  3. 3. PLATFORM GOALS‣ Democratize access to intelligence tools‣ Structure unstructured data feeds‣ Data-mine overwhelming realtime datasets‣ Surface signal & suppress noise‣ Identify and rate authoritative users & sources‣ Easy to use tools & applications for curating data on the user’s terms
  4. 4. “It’s not information overload. It’s filter failure.” - Clay Shirky
  5. 5. Sweeper - User Interface
  6. 6. The Brain as an API ‣ Breaks content (data) into pieces ‣ Analyzes pieces separately ‣ Conditionally prioritizes ‣ Learns from experience ‣ Processing is distributed ‣ Recombination of pieces
  7. 7. APIs Applications‣ Tagging API - parses text and adds taxonomy‣ Location API - detects origin location of content ‣ Sweeper - sweep, structure and sort‣ Influence API - measures influence of content online realtime data-streams‣ Reputation API - stores information about user ‣ SwiftMeme - meme/keyword tracker, behavior content discovery‣ Duplication Filter API - receives feeds and filters out ‣ SwiftMail - sort email by relevance duplicate content to cut down on retweets
  8. 8. Product (RED)
  9. 9. Queensland - ABC Australia Deployment
  10. 10. RIVER IDGlobal Trust and Reputation Server Web Services
  11. 11. WHAT IS RIVER ID?• Opt-in product for Ushahidi deployments• Collect information on all contributors• Use contributions to build trust profile• Use trust profile to help validate information in the future• Global trust bank built on OAUTH standards
  12. 12. REVERBERATIONS Measuring Influence Web Services
  13. 13. INFORMATION REVERBERATES• Good information and Bad information spread the same• Reverberations tracks influences• Breadcrumb trails for information and content
  14. 14. AUTO-TAGGINGSILCC: SwiftRiver Language Computation Core Web Services
  15. 15. WHAT IS SILCC?•Swift Language Computation Component•One of the SwiftRiver Web Services•Open Web API•Semantic Tagging of Short Text•Natural Language Processing•Multilingual•Multiple sources (twitter, email, SMS, blogs etc)•Active Learning capability
  16. 16. Swiftriver    SiLCC  Dataflow     SiSLS   Content   Items   coming   from   the   SiSLS   have     where   Swiftriver  Source     SiSLS   integrations   is   enabled     global   trust   values   Library  Service   added  to  the  object  model.     SiLCC   Swiftriver  Language   An  API  key  is  sent  along  with  the  text  to  ensure  that   the  SiLCC  is  not  open  to  any  malicious  usage.     Computational  Core     The  text  of  the   content  is  sent  to  the   SiLCC.   There  is  still  a  bit  of  ambiguity  around  what  the  NLP   should  extract  from  the  text  but  at  its  most  simple,   Using  NLP,  the  SiLCC   all  the  nouns  would  be  a  good  start.   extracts  Nouns  and   other  keywords  from   the  text.   The  SiLCC  send  back   The   lists   of   tags   sent   back   from   the   SiLCC   can   be   a  list  of  tags  that  are   added  to  the  Content  Item  along  with  any  that  were   added  to  the   extracted  from  the  source  data  by  the  parser.   Content  Item   SLISa   Although   the   NLP   tags   have   now   been   applied,   the   SLISa   is   now   responsible   for   applying   instance   Swiftriver  Language   specific  tagging  corrections.   Improvement  Service    
  17. 17. OUR GOALS•Simple Tagging of short snippets of text•Rapid tagging for high volume environments•Simple API, easy to use•Learns from user feedback•Routing of messages to upstream services•Semantic Classification•Sorts rapid streams into buckets•Clusters like messages•Visual effects•Cross-referencing
  18. 18. WHAT IT’S NOT•Does not do deep analysis of text•Only identifies words within original text
  19. 19. HOW DOES IT WORK?•Step 1: Lexical Analysis•Step 2: Parsing into constituent parts•Step 3: Part of Speech tagging•Step 4: Feature extraction•Step 5: Compute using feature weights•Lets examine each one in turn...
  20. 20. STEP 1: LEXICAL ANALYSIS•For news headlines, email subjects this is trivial, just split on spaces.•For Twitter this is more complex...
  21. 21. TWEET ANALYSIS•Tweets are surprisingly complex•Only 140 characters but many features•Emergent features from community (e.g. hashtags)•Lets take a look at a typical tweet...
  22. 22. TWEET ANALYSIS The typical Tweet: “RT @directrelief: RT @PIH: PBS @NewsHour addresses mental health needs in the aftermath of the #Haiti earthquake #health #earthquake... http://bit.ly/bNhyK6”•RT indicates a “re-tweet”•@name indicates who the original tweeter was•Multiple embedded retweets•Hashtags (e.g. #Haiti) can play two roles, as a tag and as part of the sentence
  23. 23. TWEET ANALYSIS 2•Two or more hashtags within a tweet (e.g. #health and #earthquake)•Continuation dots “...” indicates that there was more text that didn’t fit into the 140 limit somewhere in it’s history•Urls many tweets contain one or more urls As we can see this simple tweet contains no less than 7 different features and that’s not all!
  24. 24. TWEET ANALYSIS 3We want to break up the tweet into the followingparts:{ text: [PBS addresses mental health needs in the aftermath of the Haitiearthquake], hashtags: [#Haiti, #health, #earthquake], names: [@directrelief, @PIH, @NewsHour], urls: [http://bit.ly/bNhyK6],}
  25. 25. TWEET ANALYSIS 4 Why do we want to break up the tweet into parts (parsing)?•Because we want to further process the grammatically correct english text•Part of speech tagging would otherwise be corrupted by words it cannot recognize (e.g. urls, hashtags, @names etc.)•We want to save the hashtags for later use•Many of the features are irrelevant to the task of identifying tags (e.g. dots, punctuation, @name, RT)
  26. 26. TWEET ANALYSIS 5•We now take the “text” portion of the tweet and perform part of speech tagging on it•After part of speech tagging, we perform feature extraction•Features are now passed through the keyword classifier which returns a list of keywords / tags•Finally we combine these tags with the hashtags we saved earlier to give the complete tag set
  27. 27. HEADLINE AND EMAIL SUBJECT ANALYSIS•This is much simpler to do•Its a subset of the steps in Tweet Analysis•There is no parsing since there are no hashtags, @names etc.
  28. 28. FEATURE EXTRACTION• For the active learning algorithm we need to extract features to use in classification• These features should be subject/domain independent• We therefore never use the actual words as features• This would for example give artificially high weights to words such as “earthquake”• We dont want these artificial weights as we can’t foresee future disasters and we want to be as generic with classification as possible• The use of training sets does allow for domain customization if where necessary
  29. 29. FEATURE EXTRACTION• Capitalization of individual words: Either first caps, or all caps, this is an important indicator of proper nouns or other important words that make good tag candidates• Position in text: Tags seem to have a greater preponderance near the beginning of text• Part of Speech: Nouns and proper nouns are particularly important but so are some adjectives and adverbs• Capitalization of entire text: sometimes the whole text is capitalized and this should reduce overall weighting of other features• Length of the text: In shorter texts the words are more likely to be tags• The parts of speech of previous and next words (effectively this means we are using trigrams; or a window of 3)
  30. 30. TRAINING• Requires user reviewed examples• Lexical analysis, parsing and feature extraction on the examples• Multinomial naïve Bayes algorithm• NB: The granularity we are classifying is at the word level• For each word in the text, we classify it as either a keyword or not• This has pleasant side effect of providing several training examples from each user reviewed text• Even with less than 50 reviewed texts the results are comparable to the simple approach of using nouns only
  31. 31. ACTIVE LEARNING•The API also provides a method for users to send back corrected text•The corrected text is saved and then used in the next iteration of training•User may optionally specify a corpus for the example to go into•Training can be performed using any combination of corpora
  32. 32. DEVELOPER FRIENDLY•Two levels of API, the web API and the internal Python API•Either one may be used but most users will use the web API•Design is highly modular and maintainable•For very rapid backend processing the native Python API can be used
  33. 33. PYTHON CLASSESMost of the classes that make up the library aredivided into three types: 1) Tokenizers 2) Parsers 3) TaggersAll three types have consistent APIs and areinterchangeable.
  34. 34. PYTHON API•A tagger calls a parser•A parser calls a tokenizer•Output of the tokenizer goes into the parser•Output of the parser goes into the tagger•Output of the tagger goes into the user!
  35. 35. CLASSES• BasicTokenizer – This is used for splitting basic (non-tweet) text into individual words• TweetTokenizer – This is used to tokenize a tweet, it may also be used to tokenize plain text since plain text is a subset of tweets• TweetParser – Calls the TweetTokenizer and the parses the output (see previous example)• TweetTagger – Calls the TweetTokenizer and then tags the output of the text part and adds the hashtags• BasicTagger – Calls the BasicTokenizer and then tags the text, should only be used for non-tweet text, uses simple Part of Speech to identify tags• BayesTagger – Same as BasicTagger but uses weights from the naïve Bayes training algorithm
  36. 36. DEPENDANCIES•Part of speech tagging is currently performed by the Python NLTK•The Web API uses the Pylons web framework
  37. 37. CURRENT STATUS•Tag method of API is ready for use, individual deployments can choose between using the BasicTagger or the BayesTagger•Tell method (for user feedback) will be ready by the time you read this!•Training is possible on corpora of tagged data in .csv format (see examples in distribution)
  38. 38. CURRENT LIMITATIONS•Only English text is supported at the moment•Tags are always one of the words in the supplied text ie they can never be a word not in the supplied text•Very few training examples exist at the moment
  40. 40. WHAT IS GEO DICT?• For auto-mapping data• Reverse lookup lat/lon from place names• Works with data from Twitter, Email, RSS, SMS• SVM or Naive Bayes/Fisher Classification• Database of global place-names corresponding lat/lon
  41. 41. ITEM CLASSIFICATION• Feature Extraction: Bag of Words, String Kernels• Higher Level Features: Topic Modeling• Linguistic pre-processing: lemmatization, stemming• Natural Language Processing• Named Entity Recognition• Multi-class classification: One-vs.-One, One-vs.-All
  42. 42. SWIFT RIVERJon Gosier, Director of Product http://swiftly.org @swiftriver @jongos An Ushahidi Initiative