4.3 KiB
import Infobox from 'components/infobox'
Similarity is determined by comparing word vectors or "word embeddings", multi-dimensional meaning representations of a word. Word vectors can be generated using an algorithm like word2vec and usually look like this:
### banana.vector
array([2.02280000e-01, -7.66180009e-02, 3.70319992e-01,
3.28450017e-02, -4.19569999e-01, 7.20689967e-02,
-3.74760002e-01, 5.74599989e-02, -1.24009997e-02,
5.29489994e-01, -5.23800015e-01, -1.97710007e-01,
-3.41470003e-01, 5.33169985e-01, -2.53309999e-02,
1.73800007e-01, 1.67720005e-01, 8.39839995e-01,
5.51070012e-02, 1.05470002e-01, 3.78719985e-01,
2.42750004e-01, 1.47449998e-02, 5.59509993e-01,
1.25210002e-01, -6.75960004e-01, 3.58420014e-01,
# ... and so on ...
3.66849989e-01, 2.52470002e-03, -6.40089989e-01,
-2.97650009e-01, 7.89430022e-01, 3.31680000e-01,
-1.19659996e+00, -4.71559986e-02, 5.31750023e-01], dtype=float32)
To make them compact and fast, spaCy's small models (all packages
that end in sm) don't ship with word vectors, and only include
context-sensitive tensors. This means you can still use the similarity()
methods to compare documents, spans and tokens – but the result won't be as
good, and individual tokens won't have any vectors assigned. So in order to use
real word vectors, you need to download a larger model:
- python -m spacy download en_core_web_sm
+ python -m spacy download en_core_web_lg
Models that come with built-in word vectors make them available as the
Token.vector attribute. Doc.vector
and Span.vector will default to an average of their token
vectors. You can also check if a token has a vector assigned, and get the L2
norm, which can be used to normalize vectors.
### {executable="true"}
import spacy
nlp = spacy.load('en_core_web_md')
tokens = nlp(u'dog cat banana afskfsd')
for token in tokens:
print(token.text, token.has_vector, token.vector_norm, token.is_oov)
- Text: The original token text.
- has vector: Does the token have a vector representation?
- Vector norm: The L2 norm of the token's vector (the square root of the sum of the values squared)
- OOV: Out-of-vocabulary
The words "dog", "cat" and "banana" are all pretty common in English, so they're
part of the model's vocabulary, and come with a vector. The word "afskfsd" on
the other hand is a lot less common and out-of-vocabulary – so its vector
representation consists of 300 dimensions of 0, which means it's practically
nonexistent. If your application will benefit from a large vocabulary with
more vectors, you should consider using one of the larger models or loading in a
full vector package, for example,
en_vectors_web_lg, which includes over 1
million unique vectors.
spaCy is able to compare two objects, and make a prediction of how similar they are. Predicting similarity is useful for building recommendation systems or flagging duplicates. For example, you can suggest a user content that's similar to what they're currently looking at, or label a support ticket as a duplicate if it's very similar to an already existing one.
Each Doc, Span and Token comes with a
.similarity() method that lets you compare it with
another object, and determine the similarity. Of course similarity is always
subjective – whether "dog" and "cat" are similar really depends on how you're
looking at it. spaCy's similarity model usually assumes a pretty general-purpose
definition of similarity.
### {executable="true"}
import spacy
nlp = spacy.load('en_core_web_md') # make sure to use larger model!
tokens = nlp(u'dog cat banana')
for token1 in tokens:
for token2 in tokens:
print(token1.text, token2.text, token1.similarity(token2))
In this case, the model's predictions are pretty on point. A dog is very similar
to a cat, whereas a banana is not very similar to either of them. Identical
tokens are obviously 100% similar to each other (just not always exactly 1.0,
because of vector math and floating point imprecisions).