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136 lines
5.1 KiB
Plaintext
136 lines
5.1 KiB
Plaintext
//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > BASICS
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+aside("Training word vectors")
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| Dense, real valued vectors representing distributional similarity
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| information are now a cornerstone of practical NLP. The most common way
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| to train these vectors is the #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]
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| family of algorithms. If you need to train a word2vec model, we recommend
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| the implementation in the Python library
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| #[+a("https://radimrehurek.com/gensim/") Gensim].
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include ../_spacy-101/_similarity
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include ../_spacy-101/_word-vectors
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+h(3, "in-context") Similarities in context
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p
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| Aside from spaCy's built-in word vectors, which were trained on a lot of
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| text with a wide vocabulary, the parsing, tagging and NER models also
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| rely on vector representations of the #[strong meanings of words in context].
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| As the #[+a("/usage/processing-pipelines") processing pipeline] is
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| applied spaCy encodes a document's internal meaning representations as an
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| array of floats, also called a tensor. This allows spaCy to make a
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| reasonable guess at a word's meaning, based on its surrounding words.
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| Even if a word hasn't been seen before, spaCy will know #[em something]
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| about it. Because spaCy uses a 4-layer convolutional network, the
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| tensors are sensitive to up to #[strong four words on either side] of a
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| word.
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p
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| For example, here are three sentences containing the out-of-vocabulary
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| word "labrador" in different contexts.
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+code.
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doc1 = nlp(u"The labrador barked.")
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doc2 = nlp(u"The labrador swam.")
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doc3 = nlp(u"the labrador people live in canada.")
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for doc in [doc1, doc2, doc3]:
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labrador = doc[1]
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dog = nlp(u"dog")
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print(labrador.similarity(dog))
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p
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| Even though the model has never seen the word "labrador", it can make a
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| fairly accurate prediction of its similarity to "dog" in different
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| contexts.
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+table(["Context", "labrador.similarity(dog)"])
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+row
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+cell The #[strong labrador] barked.
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+cell #[code 0.56] #[+procon("yes", "similar")]
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+row
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+cell The #[strong labrador] swam.
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+cell #[code 0.48] #[+procon("no", "dissimilar")]
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+row
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+cell the #[strong labrador] people live in canada.
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+cell #[code 0.39] #[+procon("no", "dissimilar")]
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p
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| The same also works for whole documents. Here, the variance of the
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| similarities is lower, as all words and their order are taken into
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| account. However, the context-specific similarity is often still
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| reflected pretty accurately.
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+code.
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doc1 = nlp(u"Paris is the largest city in France.")
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doc2 = nlp(u"Vilnius is the capital of Lithuania.")
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doc3 = nlp(u"An emu is a large bird.")
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for doc in [doc1, doc2, doc3]:
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for other_doc in [doc1, doc2, doc3]:
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print(doc.similarity(other_doc))
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p
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| Even though the sentences about Paris and Vilnius consist of different
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| words and entities, they both describe the same concept and are seen as
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| more similar than the sentence about emus. In this case, even a misspelled
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| version of "Vilnius" would still produce very similar results.
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+table
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- 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]}
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- var counter = 0
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+row
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+row
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+cell
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for _, label in examples
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+cell=label
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each cells, label in examples
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+row(counter ? null : "divider")
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+cell=label
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for cell in cells
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+cell.u-text-center
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- var result = cell < 0.7 ? ["no", "dissimilar"] : cell != 1 ? ["yes", "similar"] : ["neutral", "identical"]
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| #[code=cell.toFixed(2)] #[+procon(...result)]
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- counter++
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p
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| Sentences that consist of the same words in different order will likely
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| be seen as very similar – but never identical.
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+code.
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docs = [nlp(u"dog bites man"), nlp(u"man bites dog"),
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nlp(u"man dog bites"), nlp(u"dog man bites")]
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for doc in docs:
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for other_doc in docs:
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print(doc.similarity(other_doc))
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p
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| Interestingly, "man bites dog" and "man dog bites" are seen as slightly
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| more similar than "man bites dog" and "dog bites man". This may be a
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| conincidence – or the result of "man" being interpreted as both sentence's
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| subject.
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+table
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- 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]}
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- var counter = 0
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+row("head")
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+cell
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for _, label in examples
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+cell.u-text-center=label
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each cells, label in examples
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+row(counter ? null : "divider")
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+cell=label
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for cell in cells
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+cell.u-text-center
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- var result = cell < 0.7 ? ["no", "dissimilar"] : cell != 1 ? ["yes", "similar"] : ["neutral", "identical"]
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| #[code=cell.toFixed(2)] #[+procon(...result)]
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- counter++
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