January 2018
Beginner to intermediate
284 pages
8h 35m
English
Content preview from Deep Learning Essentials
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Sequence tagging
Sequence tagging can be understood as a problem where the model sees a sequence of words or tokens for each word in the sequence and it is expected to emit a label. In other words, the model is expected to tag the whole sequence of tokens with an appropriate label from a known label dictionary. Sequence tagging has some very interesting applications in natural language understanding such as named entity recognition, part of speech tagging, and so on:
- Named Entity Recognition: Also known as NER, Named Entity Recognition is an information extraction technique that aims to recognize named entities in the given sequence of text tokens (for example, words). Some of the common named entities include person, location, organization, ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.