Use Query Tagging Method to Improve Traditional and Voice Search Shopping Results

MCC Room #201

2:45 pm - 3:30 pm

HOW THIS FITS INTO IOT
Filter out noises or unimportant words/phrases, take the advantage of high accuracy pre-train word embeddings and combine bi-directional LSTM models to solve both traditional web search engine or phone app voice search problems.

WHAT ATTENDEES BRING
Your curiosity and open mind

WHAT ATTENDEES WILL LEARN
How to filter out unimportant words/phrases, why it is important

Differences of Word2Vec and Sequential Word Embeddings, Pros and Cons

How to collect training data in economical ways, and train NLP model with limited size of training data

Tips for model deployment

KNOWLEDGE REQUIRED
Any technical levels are welcome. Basic knowledge on Possibility, TensorFlow, JSON would be recommended, but not required.

LINKS & READING MATERIAL
Deep Speech: Scaling up end-to-end speech recognition — https://arxiv.org/abs/1412.5567

Deep contextualized word representations –https://arxiv.org/abs/1802.05365

GloVe: Global Vectors for Word Representation –https://nlp.stanford.edu/pubs/glove.pdf

Attention is All You Need — https://arxiv.org/abs/1706.03762

Basic Solr Concepts –http://www.solrtutorial.com/basic-solr-concepts.html

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