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Victoria Ubaldo @Vikyale Introduction to NLP : How to improve accessibility with Machine Learning CONNECT REIMAGINE 2021

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Hello! Computer and System Engineer. Peruvian Data Analyst @Interbank Msc Candidate Computer Science, PUCP. Enjoy mentoring, illustration and dancing!

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Agenda ● Machine Learning and Accessibility ● Natural Language Processing ● Tensorflow for NLP ● Next steps

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Machine Learning is programming with data

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Natural Language Process (NLP)

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Where find NLP ? Chatbots Predictive text (autocomplete) Transcription Language translation Others: Text Analysis Email filters Search Result Sentiment Analysis

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Challenges in NLP

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Challenges in NLP : Textual Accessibility Visual Auditory Speech

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Accessible = Comprehensible

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Challenges in NLP : Textual Accessibility Transcription Speech recognition Language Translation

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Case : Recognize sentiment in Text

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Amazon and Yelp reviews 1 : Good 0: Bad

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Pre-processing data

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Text Processing

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Tokenization

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

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Stop Words

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Stemming

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Lemmatization

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Padding

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Tools! ● Python 3.+ ● Jupyter Notebook or Google Colab ● and using a framework for ML

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¿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.

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Trying in Google Colab bit.ly/2TU9MJz

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Challenges in NLP : Textual Accessibility Transcription Speech recognition

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Challenges in NLP : Textual Accessibility Language Translation

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Next steps Try new tools : - Recurrent Neural Network (RNN) - Long short-term memory (LSTM)

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Next steps Increase your corpus with diverse data. - PDFMiner - Tweepy API

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Next steps Datasets with native or indigenous language or code mixing

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“We can build a much brighter future where humans are relieved of menial work using AI capabilities” - Andrew Ng

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Thanks! @womenwhocode #WWCode Keep in touch! linkedin.com/in/victoriaubaldo @vikyale

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