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Natural language Analysis
This document provides an overview of language analysis using machine learning, detailing its applications across various domains, including natural language processing and sentiment analysis. It highlights significant advancements in machine learning, such as neural networks and reinforcement learning, and references notable examples like AlphaGo and machine-generated news articles. The author, Rudradeb Mitra, shares insights from his extensive background in AI research and emphasizes the transformative potential of machine learning in today's data-rich environment.
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Natural language Analysis
- 1. Language Analysis using Machinelearning: An overview Rudradeb Mitra
- 2. Bio • AI researcherpublished 10 research papers on topics like logical reasoning, language analysis, Semantic web. • Masters from University of Cambridge, UK. • Involved with startups since 2010. • Machine learning enthusiast.
- 3. What is machinelearning? • Apply previously acquired knowledge to new or novel situation • Search tree, neural network, Bayesian reasoning, logic,… • Boom and AI winter cycle (1974-80, 1980-87) Arthur Samuel with his checker playing machine
- 4. But something ishappening recently….. • AlphaGo defeated world Go champion. • AP is going to use machine created news articles for sports coverage • Deep Mind to check NHS eye scans for disease analysis • People have termed it similar to Industrial revolution
- 5. Applications • News articles(AP), robot lawyers, designers (wix), car industry (google, apple), tour guides, rockets…. • Open AI, facebook, google, microsoft, twitter… • ….machine learning will affect all domains…..
- 6. Why now? • Bigdata - What do we do with it? • Visualize, Analyze - Human element • Machine learning / Deep neural network - Learn from the data
- 7. Language understanding • “Acomputer would deserve to be called intelligent if it could deceive a human into believing that it is human.” - Alan Turing • Language is the form of communication. • Basic necessity in solving AI problems in language understanding.
- 8. Applications of NLP •Topic modelling • Text summarization • Translation • Sentiment analysis • Image captions and descriptions …
- 9. Historic approaches… • Syntaxtree • Semantic - RDF, OWL • LSA - bag of words, similar words appear together. Concepts are represented as patterns of words.
- 10. Latent Dirichlet Analysis •Start with document, bag of words and K topics • Output - Documents are of what topics (in %) • Randomly/semi-randomly assign each word to a topic • All topic assignments except for the current word in question are correct • Improve by reassign ‘w’ a new topic. Choose topic t with probability p(topic t | document d) * p(word w | topic t)
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- 13. Deep Neural network •Many layers / neurons • Wide / Deep network • Data + computations power
- 14. CBOW / skipgram
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- 16. word2vec • Applications: Sentenceauto fill, playlist, genes… • Issues: Local context. Missing global context. • GloVe
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- 22. AlphaGo - Buildingintuition • Took 150,000 games played by good human players and used an artificial neural network to find patterns • Learned to predict with high probability what move a human player would take • Play against itself, to get an estimate of how likely a given board position was to be a winning one - Reinforcement learning • No detailed knowledge of Go. Instead analyzed thousands of prior games and engaged in a lot of self- play.
- 23. Thank You Questions? [email protected] Feel freeto email or add me on linkedin
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