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1
Introduction
2
Evolution of humancomputer interaction
3
Applications of computers
4
NLP
5
Sentiment Analysis
6
Traditional Approach
7
Stanford Sentiment Analysis
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Classification
9
Training
10
Feature Extractor
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Dialogue Classification
12
Extract Relations
13
Example
14
Challenges
15
Ambiguity
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Ambiguous
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How we solve ambiguity
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Engram tagging
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Words around it
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Summary
21
Limitations
22
Unsupervised Training
23
Neural Nets
24
Data and Training
25
Conclusion
Description:
Explore the future of human-computer interaction in this conference talk from WeAreDevelopers Conference 2017. Delve into the evolution of voice interfaces like Siri, Amazon Alexa, and Google Home, and understand why voice commands are becoming the preferred method of interaction. Learn about Natural Language Processing (NLP), sentiment analysis, and traditional approaches to computer understanding. Discover the Stanford Sentiment Analysis Classification method, feature extraction techniques, and dialogue classification. Examine the challenges of ambiguity in language processing and how they are addressed through engram tagging and contextual analysis. Gain insights into the limitations of current approaches and the potential of unsupervised training and neural networks in advancing computer understanding. Conclude with a look at the data and training requirements for these cutting-edge technologies.

Getting Computers to Understand Us

WeAreDevelopers
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