most_similar

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
38/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
python

Research direction

Start at the most_similar API described in the issue and inspect how sense2vec keys encode terms and POS tags. Determine whether similarity filtering can target a requested POS, then verify that a verb query returns verb-tagged results such as chase|VERB or bark|VERB without changing the existing noun behavior.

Written by the indexing model from the issue text.

Description

Is there a way to specify the POS tag of the results you are looking for.
Example: s2v.most_similar("dog|NOUN", n=10) returns [('puppy|NOUN', 0.9188), ('cat|NOUN', 0.9176), ('kitten|NOUN', 0.9081), ('pup|NOUN', 0.9074), ('own_dog|NOUN', 0.9028), ('other_dog|NOUN', 0.902), ('little_dog|NOUN', 0.8998), ('ferret|NOUN', 0.8952), ('chihuahua|NOUN', 0.8862), ('husky|NOUN', 0.886)]
but what if I want words that are similar but are verbs. In that case I would expect to have 'chase|VERB' or 'bark|verb'. Is it possible to do that?

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Python
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