mirror of https://github.com/explosion/spaCy.git
136 lines
5.1 KiB
Plaintext
136 lines
5.1 KiB
Plaintext
//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > BASICS
|
||
|
||
+aside("Training word vectors")
|
||
| 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. If you need to train a word2vec model, we recommend
|
||
| the implementation in the Python library
|
||
| #[+a("https://radimrehurek.com/gensim/") Gensim].
|
||
|
||
include ../_spacy-101/_similarity
|
||
include ../_spacy-101/_word-vectors
|
||
|
||
+h(3, "in-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 #[+a("/usage/processing-pipelines") processing pipeline] is
|
||
| applied spaCy 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("yes", "similar")]
|
||
|
||
+row
|
||
+cell The #[strong labrador] swam.
|
||
+cell #[code 0.48] #[+procon("no", "dissimilar")]
|
||
|
||
+row
|
||
+cell the #[strong labrador] people live in canada.
|
||
+cell #[code 0.39] #[+procon("no", "dissimilar")]
|
||
|
||
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
|
||
- var result = cell < 0.7 ? ["no", "dissimilar"] : cell != 1 ? ["yes", "similar"] : ["neutral", "identical"]
|
||
| #[code=cell.toFixed(2)] #[+procon(...result)]
|
||
- 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("head")
|
||
+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
|
||
- var result = cell < 0.7 ? ["no", "dissimilar"] : cell != 1 ? ["yes", "similar"] : ["neutral", "identical"]
|
||
| #[code=cell.toFixed(2)] #[+procon(...result)]
|
||
- counter++
|