New answers tagged natural-language-processing
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For deep learning models, embedding vectors have become the standard way of encoding text features almost immediately after their introduction.
The reason for this is that neural networks work with data encoded with continuous values ranging from 0 to 1 (or sometimes from -1 to 1). Bag of Words and TF-IDF can be modified to produce values in this range, but ...
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Since sport commentaries are a fairly restricted domain, and the language does not vary much, I would go for a canned text approach.
Analyse what kind of events you get, and what variables you're dealing with. Then write some template sentences with placeholders for the variables. The more you write for the same data, the more varied your text will be. You ...
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In my experience with NER with Spacy, and disagreeing with this stackoverflow solution
Having said that, as @polm23 rightly mentioned, a several thousand samples for each entity should generate/predict entities, otherwise spacy would just recognise them based on default spacy entity types (mainly 'work-of-art')
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Caveat: I am not a native English speaker (but French). And mostly interested in symbolic artificial intelligence (the topic of my PhD thesis defended in 1990; see books by Jacques Pitrat)
Encoding is related to decoding. Most of the time, if you encode something A into some other thing B, you can "decode" B to get back A.
Otherwise, you would just ...
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From my experience with reading papers and books, I think these two terms are sometimes used interchangeably.
As you also point out, an encoder (in an auto-encoder) also may also learn some "semantics" of the inputs in order to produce the latent space. However, the way encoders are trained may not produce embeddings, with similar properties to e.g....
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There is no specific number of labels that is "enough". For simple cases you can start with a few hundred examples, but normally you'll want several thousand.
Since you have a large number of classes your problem might be a harder one, but on the other hand it could be easy if most of your text is like "This merchant is called XXX".
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