spaCy/website/docs/usage/word-vectors-similarities.jade
Savva Kolbachev 800a8faff4 Changed the capital of Lithuania to Vilnius
Hi,
There is a typo about the capital of Lithuania.

Vilnius is the capital of Lithuania https://en.wikipedia.org/wiki/Vilnius
Ljubljana is the capital of Slovenia https://en.wikipedia.org/wiki/Ljubljana
2017-06-12 23:27:00 +03:00

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//- 💫 DOCS > USAGE > WORD VECTORS & SIMILARITIES
include ../../_includes/_mixins
p
| Dense, real valued vectors representing distributional similarity
| information are now a cornerstone of practical NLP. The most common way
| to train these vectors is the #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]
| family of algorithms. The default
| #[+a("/docs/usage/models#available") English model] installs
| 300-dimensional vectors trained on the
| #[+a("http://commoncrawl.org") Common Crawl] corpus.
+aside("Tip: Training a word2vec model")
| If you need to train a word2vec model, we recommend the implementation in
| the Python library #[+a("https://radimrehurek.com/gensim/") Gensim].
+h(2, "101") Similarity and word vectors 101
+tag-model("vectors")
include _spacy-101/_similarity
include _spacy-101/_word-vectors
+h(2, "similarity-context") Similarities in context
p
| Aside from spaCy's built-in word vectors, which were trained on a lot of
| text with a wide vocabulary, the parsing, tagging and NER models also
| rely on vector representations of the #[strong meanings of words in context].
| As the first component of the
| #[+a("/docs/usage/language-processing-pipeline") processing pipeline], the
| tensorizer encodes a document's internal meaning representations as an
| array of floats, also called a tensor. This allows spaCy to make a
| reasonable guess at a word's meaning, based on its surrounding words.
| Even if a word hasn't been seen before, spaCy will know #[em something]
| about it. Because spaCy uses a 4-layer convolutional network, the
| tensors are sensitive to up to #[strong four words on either side] of a
| word.
p
| For example, here are three sentences containing the out-of-vocabulary
| word "labrador" in different contexts.
+code.
doc1 = nlp(u"The labrador barked.")
doc2 = nlp(u"The labrador swam.")
doc3 = nlp(u"the labrador people live in canada.")
for doc in [doc1, doc2, doc3]:
labrador = doc[1]
dog = nlp(u"dog")
print(labrador.similarity(dog))
p
| Even though the model has never seen the word "labrador", it can make a
| fairly accurate prediction of its similarity to "dog" in different
| contexts.
+table(["Context", "labrador.similarity(dog)"])
+row
+cell The #[strong labrador] barked.
+cell #[code 0.56] #[+procon("pro")]
+row
+cell The #[strong labrador] swam.
+cell #[code 0.48] #[+procon("con")]
+row
+cell the #[strong labrador] people live in canada.
+cell #[code 0.39] #[+procon("con")]
p
| The same also works for whole documents. Here, the variance of the
| similarities is lower, as all words and their order are taken into
| account. However, the context-specific similarity is often still
| reflected pretty accurately.
+code.
doc1 = nlp(u"Paris is the largest city in France.")
doc2 = nlp(u"Vilnius is the capital of Lithuania.")
doc3 = nlp(u"An emu is a large bird.")
for doc in [doc1, doc2, doc3]:
for other_doc in [doc1, doc2, doc3]:
print(doc.similarity(other_doc))
p
| Even though the sentences about Paris and Vilnius consist of different
| words and entities, they both describe the same concept and are seen as
| more similar than the sentence about emus. In this case, even a misspelled
| version of "Vilnius" would still produce very similar results.
+table
- var examples = {"Paris is the largest city in France.": [1, 0.85, 0.65], "Vilnius is the capital of Lithuania.": [0.85, 1, 0.55], "An emu is a large bird.": [0.65, 0.55, 1]}
- var counter = 0
+row
+row
+cell
for _, label in examples
+cell=label
each cells, label in examples
+row(counter ? null : "divider")
+cell=label
for cell in cells
+cell.u-text-center #[code=cell.toFixed(2)]
| #[+procon(cell < 0.7 ? "con" : cell != 1 ? "pro" : "neutral")]
- counter++
p
| Sentences that consist of the same words in different order will likely
| be seen as very similar but never identical.
+code.
docs = [nlp(u"dog bites man"), nlp(u"man bites dog"),
nlp(u"man dog bites"), nlp(u"dog man bites")]
for doc in docs:
for other_doc in docs:
print(doc.similarity(other_doc))
p
| Interestingly, "man bites dog" and "man dog bites" are seen as slightly
| more similar than "man bites dog" and "dog bites man". This may be a
| conincidence or the result of "man" being interpreted as both sentence's
| subject.
+table
- var examples = {"dog bites man": [1, 0.9, 0.89, 0.92], "man bites dog": [0.9, 1, 0.93, 0.9], "man dog bites": [0.89, 0.93, 1, 0.92], "dog man bites": [0.92, 0.9, 0.92, 1]}
- var counter = 0
+row
+row
+cell
for _, label in examples
+cell.u-text-center=label
each cells, label in examples
+row(counter ? null : "divider")
+cell=label
for cell in cells
+cell.u-text-center #[code=cell.toFixed(2)]
| #[+procon(cell < 0.7 ? "con" : cell != 1 ? "pro" : "neutral")]
- counter++
+h(2, "custom") Customising word vectors
+under-construction
p
| By default, #[+api("token#vector") #[code Token.vector]] returns the
| vector for its underlying #[+api("lexeme") #[code Lexeme]], while
| #[+api("doc#vector") #[code Doc.vector]] and
| #[+api("span#vector") #[code Span.vector]] return an average of the
| vectors of their tokens. You can customize these
| behaviours by modifying the #[code doc.user_hooks],
| #[code doc.user_span_hooks] and #[code doc.user_token_hooks]
| dictionaries.