Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Introduction to NLP : How to improve accessibility with Machine Learning

Introduction to NLP : How to improve accessibility with Machine Learning

WWCode CONNECT REIMAGINE 2021

A92db2207ad52fcd8693b731c4686575?s=128

Victoria Ubaldo

June 11, 2021
Tweet

Transcript

  1. None
  2. Victoria Ubaldo @Vikyale Introduction to NLP : How to improve

    accessibility with Machine Learning CONNECT REIMAGINE 2021
  3. Hello! Computer and System Engineer. Peruvian Data Analyst @Interbank Msc

    Candidate Computer Science, PUCP. Enjoy mentoring, illustration and dancing!
  4. Agenda • Machine Learning and Accessibility • Natural Language Processing

    • Tensorflow for NLP • Next steps
  5. None
  6. None
  7. None
  8. Machine Learning is programming with data

  9. None
  10. None
  11. Natural Language Process (NLP)

  12. None
  13. Where find NLP ? Chatbots Predictive text (autocomplete) Transcription Language

    translation Others: Text Analysis Email filters Search Result Sentiment Analysis
  14. Challenges in NLP

  15. None
  16. Challenges in NLP : Textual Accessibility Visual Auditory Speech

  17. Accessible = Comprehensible

  18. Challenges in NLP : Textual Accessibility Transcription Speech recognition Language

    Translation
  19. Case : Recognize sentiment in Text

  20. None
  21. Amazon and Yelp reviews 1 : Good 0: Bad

  22. Pre-processing data

  23. Text Processing

  24. Tokenization

  25. Out Of Vocabulary (OOV) EXTRA WORDS unknown tokens (UNK)

  26. Stop Words

  27. Stemming

  28. Lemmatization

  29. Padding

  30. Tools! • Python 3.+ • Jupyter Notebook or Google Colab

    • and using a framework for ML
  31. ¿Why Tensorflow? Whether you’re an expert or a beginner, TensorFlow

    is an end-to-end platform that makes it easy for you to build and deploy ML models.
  32. Trying in Google Colab bit.ly/2TU9MJz

  33. Challenges in NLP : Textual Accessibility Transcription Speech recognition

  34. None
  35. Challenges in NLP : Textual Accessibility Language Translation

  36. None
  37. Next steps Try new tools : - Recurrent Neural Network

    (RNN) - Long short-term memory (LSTM)
  38. Next steps Increase your corpus with diverse data. - PDFMiner

    - Tweepy API
  39. Next steps Datasets with native or indigenous language or code

    mixing
  40. “We can build a much brighter future where humans are

    relieved of menial work using AI capabilities” - Andrew Ng
  41. Thanks! @womenwhocode #WWCode Keep in touch! linkedin.com/in/victoriaubaldo @vikyale

  42. None