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

MCC Room #201

2:45 pm - 3:30 pm

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.

Your curiosity and open mind

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

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

Deep Speech: Scaling up end-to-end speech recognition —

Deep contextualized word representations –

GloVe: Global Vectors for Word Representation –

Attention is All You Need —

Basic Solr Concepts –

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