One cool use of NLP is for NER (Named-entity recognition)
This enables you to find person names, organizations, locations, quantities, monetary values, percentages, etc. in a piece of text.
If you only want to use it on your data this API cloud.google.com/natural-langua… can help
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This enables you to find person names, organizations, locations, quantities, monetary values, percentages, etc. in a piece of text.
If you only want to use it on your data this API cloud.google.com/natural-langua… can help
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Sometimes you need to create your own model for your specific data corpus (eg: legal, science, medical texts)
To create your own model, AutoML Natural Language can help you:
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To create your own model, AutoML Natural Language can help you:
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If you want to build everything from scratch, then you'll need:
• a language embedding (like BERT, ELMO, USE) and #TFHub have all you need
• a dataset and this github.com/juand-r/entity… can help you find one
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• a language embedding (like BERT, ELMO, USE) and #TFHub have all you need
• a dataset and this github.com/juand-r/entity… can help you find one
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• A neural network that can understand the text structure and sequence of words.
This is usually done with RNN (Recurrent Neural Networks) or
Even better, a LSTM (Long Short Term Memory)
This tutorial can help you:
towardsdatascience.com/named-entity-r…
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This is usually done with RNN (Recurrent Neural Networks) or
Even better, a LSTM (Long Short Term Memory)
This tutorial can help you:
towardsdatascience.com/named-entity-r…
4/4🧵
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