- Using deep learning, we are able to understand text better across multiple languages and use labeled data much more efficiently than traditional NLP techniques.
- It’s reasonable to assume that the posts on these pages will represent a dedicated topic – for example, posts on the Steelers page will contain text about the Steelers football team.
- With deep learning, we can instead use “word embeddings,” a mathematical concept that preserves the semantic relationship among words.
- Written language, despite the variations mentioned above, has a lot of structure that can be extracted from unlabeled text using unsupervised learning and captured in embeddings.
- Text understanding on Facebook requires solving tricky scaling and language challenges where traditional NLP techniques are not effective.
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@troykelly: “Introducing DeepText: Facebook’s text understanding engine #MachineLearning #NLP”
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