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1
Intro
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ABOUT BASIS TECHNOLOGY
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Rosette Capabilities
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Names are a Challenge
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Task: Name Matching
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Name Matching Algorithms
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Step One: Modeling Sequences of Characters
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Step Two: Modeling Transliterations
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Issues with HMM-Based Name Matching
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What does an HMM Actually Do?
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How Would You Transliterate a Name?
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The Antidote: Sequence-to-Sequence (seq2seq)
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Neural Network of Choice: Long Short-Term Memory (LSTM) Cells
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Step One: Learning to Transliterate with seq2seq
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Step Two: Running the Transliterator in Reverse to Score
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How Can We Produce a Score?
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Processing Time on Name Pairs (seconds)
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Faster seq2seq with a Convolutional Neural Network CNNO
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Convolutional Neural Net (CNN)
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CNN in Natural Language Processing
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What Does This Tell Us?
Description:
Explore the challenges and innovative solutions in cross-language name matching during this 59-minute webinar on neural network applications. Delve into the limitations of traditional methods like edit distance and Hidden Markov Models, and discover how deep neural networks significantly improve accuracy in English/Japanese name matching. Learn about sequence-to-sequence models, Long Short-Term Memory cells, and Convolutional Neural Networks as applied to transliteration and name scoring. Gain insights into BasisTech's Rosette capabilities and the practical implications of these advanced techniques for consumer and governmental domains.

Understanding Names with Neural Networks - Session 2

BasisTech
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