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Dec 4-5, 2018 | York University

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Challenge 5 nlp

  1. 1. Challenge: Communicating with communities Natural language processing? International Rescue Committee Toronto December 4-5, 2018
  2. 2. Challenge To collect and record and analyze and respond to information, queries, and concerns from the affected population. When you are limited in terms of time, human resources, and financial resources, what are non-traditional ways to do surveys, collect and respond to feedback?
  3. 3. Our goals To provide more ways to provide input into a humanitarian response- throughout the duration of a response To receive feedback or information about services in a timely and comprehensive manner To respond more quickly to feedback To scale feedback/engagement mechanisms To more quickly receive information about the affected population’s needs For the affected population: For the humanitarian actor:
  4. 4. Traditional methods Hotline HR intensive Requires reliable network coverage Satisfaction box Require literacy Often go and unchecked One-on-one surveys or interviews HR intensive May require additional data entry, translation and analysis Focus group discussions or community meetings HR intensive Infrequent Require data entry, transcription, translation, and analysis Comment cards Require literacy Require data entry and translation
  5. 5. Why the need for alternatives to traditional methods?  Traditional methods for ongoing community engagement can be human resource intensive  Heavy dependence on literacy, phone ownership and network coverage  Considerable time is needed for data entry, translation and/or transcription, and analysis  The affected population may be geographically dispersed – reaching them in-person may not be an option.
  6. 6. Challenge/Solution areas Providing Information about available services Surveys – needs assessments, satisfaction surveys, focus group discussions, key informant interviews Feedback collection and response
  7. 7. Key features of a functional solution Must offer anonymity to users Must be simple enough to “learn” in a day or two (from the NGO side) Must work for multiple languages and/or dialects Must be inclusive of or accessible by multiple “types” of people (women, low literacy, etc.)
  8. 8. Possible solution: Natural Language Processing What is NLP? What could it do for us? Automatic transcription Automatic or machine translation Sentiment analysis Topic segmentation and recognition . Speech recognition Suggestions Needs Comments Complaints Clean, usable, data
  9. 9. Possible hurdles to overcome Why do you want to know?Bonjour ? Hello?

#emergencydatascience Dec 4-5, 2018 | York University


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