mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 11:29:13 +03:00
031f6c7b60
* Update spaCy badge `alt` text * Add `next/image` component to Universe * Add missing `alt`texts
151 lines
7.0 KiB
Plaintext
151 lines
7.0 KiB
Plaintext
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](https://en.wikipedia.org/wiki/Word2vec) and usually look like this:
|
||
|
||
```python {title="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)
|
||
```
|
||
|
||
<Infobox title="Important note" variant="warning">
|
||
|
||
To make them compact and fast, spaCy's small [pipeline packages](/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 pipeline package:
|
||
|
||
```diff
|
||
- python -m spacy download en_core_web_sm
|
||
+ python -m spacy download en_core_web_lg
|
||
```
|
||
|
||
</Infobox>
|
||
|
||
Pipeline packages that come with built-in word vectors make them available as
|
||
the [`Token.vector`](/api/token#vector) attribute.
|
||
[`Doc.vector`](/api/doc#vector) and [`Span.vector`](/api/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.
|
||
|
||
```python {executable="true"}
|
||
import spacy
|
||
|
||
nlp = spacy.load("en_core_web_md")
|
||
tokens = nlp("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 pipeline'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 pipeline packages or
|
||
loading in a full vector package, for example,
|
||
[`en_core_web_lg`](/models/en#en_core_web_lg), which includes **685k 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`](/api/doc), [`Span`](/api/span), [`Token`](/api/token) and
|
||
[`Lexeme`](/api/lexeme) comes with a [`.similarity`](/api/token#similarity)
|
||
method that lets you compare it with another object, and determine the
|
||
similarity. Of course similarity is always subjective – whether two words, spans
|
||
or documents are similar really depends on how you're looking at it. spaCy's
|
||
similarity implementation usually assumes a pretty general-purpose definition of
|
||
similarity.
|
||
|
||
> #### 📝 Things to try
|
||
>
|
||
> 1. Compare two different tokens and try to find the two most _dissimilar_
|
||
> tokens in the texts with the lowest similarity score (according to the
|
||
> vectors).
|
||
> 2. Compare the similarity of two [`Lexeme`](/api/lexeme) objects, entries in
|
||
> the vocabulary. You can get a lexeme via the `.lex` attribute of a token.
|
||
> You should see that the similarity results are identical to the token
|
||
> similarity.
|
||
|
||
```python {executable="true"}
|
||
import spacy
|
||
|
||
nlp = spacy.load("en_core_web_md") # make sure to use larger package!
|
||
doc1 = nlp("I like salty fries and hamburgers.")
|
||
doc2 = nlp("Fast food tastes very good.")
|
||
|
||
# Similarity of two documents
|
||
print(doc1, "<->", doc2, doc1.similarity(doc2))
|
||
# Similarity of tokens and spans
|
||
french_fries = doc1[2:4]
|
||
burgers = doc1[5]
|
||
print(french_fries, "<->", burgers, french_fries.similarity(burgers))
|
||
```
|
||
|
||
### What to expect from similarity results {id="similarity-expectations"}
|
||
|
||
Computing similarity scores can be helpful in many situations, but it's also
|
||
important to maintain **realistic expectations** about what information it can
|
||
provide. Words can be related to each other in many ways, so a single
|
||
"similarity" score will always be a **mix of different signals**, and vectors
|
||
trained on different data can produce very different results that may not be
|
||
useful for your purpose. Here are some important considerations to keep in mind:
|
||
|
||
- There's no objective definition of similarity. Whether "I like burgers" and "I
|
||
like pasta" is similar **depends on your application**. Both talk about food
|
||
preferences, which makes them very similar – but if you're analyzing mentions
|
||
of food, those sentences are pretty dissimilar, because they talk about very
|
||
different foods.
|
||
- The similarity of [`Doc`](/api/doc) and [`Span`](/api/span) objects defaults
|
||
to the **average** of the token vectors. This means that the vector for "fast
|
||
food" is the average of the vectors for "fast" and "food", which isn't
|
||
necessarily representative of the phrase "fast food".
|
||
- Vector averaging means that the vector of multiple tokens is **insensitive to
|
||
the order** of the words. Two documents expressing the same meaning with
|
||
dissimilar wording will return a lower similarity score than two documents
|
||
that happen to contain the same words while expressing different meanings.
|
||
|
||
<Infobox title="Tip: Check out sense2vec" emoji="💡">
|
||
|
||
<Image
|
||
src="/images/sense2vec.jpg"
|
||
href="https://github.com/explosion/sense2vec"
|
||
alt="sense2vec Screenshot"
|
||
/>
|
||
|
||
[`sense2vec`](https://github.com/explosion/sense2vec) is a library developed by
|
||
us that builds on top of spaCy and lets you train and query more interesting and
|
||
detailed word vectors. It combines noun phrases like "fast food" or "fair game"
|
||
and includes the part-of-speech tags and entity labels. The library also
|
||
includes annotation recipes for our annotation tool [Prodigy](https://prodi.gy)
|
||
that let you evaluate vectors and create terminology lists. For more details,
|
||
check out [our blog post](https://explosion.ai/blog/sense2vec-reloaded). To
|
||
explore the semantic similarities across all Reddit comments of 2015 and 2019,
|
||
see the [interactive demo](https://explosion.ai/demos/sense2vec).
|
||
|
||
</Infobox>
|