multi token patterns about concise-concepts HOT 12 CLOSED

I'm also thinking of a postprocessing trick now. If a token is detected as an entity, but it is part of a noun-chunk, we may also attempt to highlight the entire noun-chunk.

This would be for a separate tutorial, but I'm curious what you think of the idea.

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They are, but the n-grams do actually need to be present in the embedding model. If not, the algorithm doesn´t have any input to expand over.

I can see 2 solutions:

• I could choose to average out embeddings of the individual tokens in the n-grams. In case of "big knife" this will likely result in a lot less accurate results.
• I could choose to only include the token of the n-gram that is most likely to align with the label. In case of "big knife", this would result in "knife" aligning with "utensils" and therefore result in the embedding of "knife" to be used as fallback.

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Additionally, there is a function to add a flag include_compound_words, which should allow for the model to detect "big knife" based on only having an initial similarity result for "knife".

This I also one of the features that isn't properly added to the documentation.

Besides that, the exclude_pos and exclude_dep are too.

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I generally like to compose the behaviour of the patterns along with your rule-based matcher explorer https://demos.explosion.ai/matcher.

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Yeah, averaging the embeddings of inputs seems like it'll result in a bad time.

But it was indeed probably the include_compound_words feature that was missing from my initial trial.

There is also a third option, one that (hopefully) will get announced next week on our YouTube channel.

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Now you got me curious about the third option.

But cool that you are working on a tutorial. Let me know if there are any hiccups or features you might think of.

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@koaning I closed this for now. Will review the solution after your blogpost.

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It will be a two-part thing, the first part will be on YouTube. The thing about the solution though is that it is already implemented in another library 😉

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That library being? 😅 or are you talking about the doc.noun_chunks part?

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https://twitter.com/explosion_ai/status/1579840012174204928

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Cool. I´ll do some testing and look into a way to integrate this.

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There are likely some other integrations inbound, but yeah, s2v is a great trick.

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