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260 lines
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Plaintext
260 lines
8.6 KiB
Plaintext
//- 💫 DOCS > USAGE > SPACY'S DATA MODEL
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include ../../_includes/_mixins
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p After reading this page, you should be able to:
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+list
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+item Understand how spaCy's Doc, Span, Token and Lexeme object work
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+item Start using spaCy's Cython API
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+item Use spaCy more efficiently
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+h(2, "design-considerations") Design considerations
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+h(3, "no-job-too-big") No job too big
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p
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| When writing spaCy, one of my mottos was #[em no job too big]. I wanted
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| to make sure that if Google or Facebook were founded tomorrow, spaCy
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| would be the obvious choice for them. I wanted spaCy to be the obvious
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| choice for web-scale NLP. This meant sweating about performance, because
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| for web-scale tasks, Moore's law can't save you.
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p
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| Most computational work gets less expensive over time. If you wrote a
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| program to solve fluid dynamics in 2008, and you ran it again in 2014,
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| you would expect it to be cheaper. For NLP, it often doesn't work out
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| that way. The problem is that we're writing programs where the task is
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| something like "Process all articles in the English Wikipedia". Sure,
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| compute prices dropped from $0.80 per hour to $0.20 per hour on AWS in
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| 2008-2014. But the size of Wikipedia grew from 3GB to 11GB. Maybe the
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| job is a #[em little] cheaper in 2014 — but not by much.
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+h(3, "annotation-layers") Multiple layers of annotation
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p
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| When I tell a certain sort of person that I'm a computational linguist,
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| this comic is often the first thing that comes to their mind:
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+image("http://i.imgur.com/n3DTzqx.png", 450)
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+image-caption © #[+a("http://xkcd.com") xkcd]
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p
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| I've thought a lot about what this comic is really trying to say. It's
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| probably not talking about #[em data models] — but in that sense at
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| least, it really rings true.
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p
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| You'll often need to model a document as a sequence of sentences. Other
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| times you'll need to model it as a sequence of words. Sometimes you'll
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| care about paragraphs, other times you won't. Sometimes you'll care
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| about extracting quotes, which can cross paragraph boundaries. A quote
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| can also occur within a sentence. When we consider sentence structure,
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| things get even more complicated and contradictory. We have syntactic
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| trees, sequences of entities, sequences of phrases, sub-word units,
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| multi-word units...
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p
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| Different applications are going to need to query different,
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| overlapping, and often contradictory views of the document. They're
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| often going to need to query them jointly. You need to be able to get
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| the syntactic head of a named entity, or the sentiment of a paragraph.
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+h(2, "solutions") Solutions
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+h(3) Fat types, thin tokens
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+h(3) Static model, dynamic views
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p
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| Different applications are going to need to query different,
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| overlapping, and often contradictory views of the document. For this
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| reason, I think it's a bad idea to have too much of the document
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| structure reflected in the data model. If you structure the data
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| according to the needs of one layer of annotation, you're going to need
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| to copy the data and transform it in order to use a different layer of
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| annotation. You'll soon have lots of copies, and no single source of
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| truth.
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+h(3) Never go full stand-off
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+h(3) Implementation
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+h(3) Cython 101
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+h(3) #[code cdef class Doc]
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p
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| Let's start at the top. Here's the memory layout of the
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| #[+api("doc") #[code Doc]] class, minus irrelevant details:
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+code.
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from cymem.cymem cimport Pool
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from ..vocab cimport Vocab
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from ..structs cimport TokenC
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cdef class Doc:
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cdef Pool mem
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cdef Vocab vocab
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cdef TokenC* c
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cdef int length
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cdef int max_length
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p
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| So, our #[code Doc] class is a wrapper around a TokenC* array — that's
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| where the actual document content is stored. Here's the #[code TokenC]
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| struct, in its entirety:
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+h(3) #[code cdef struct TokenC]
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+code.
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cdef struct TokenC:
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const LexemeC* lex
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uint64_t morph
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univ_pos_t pos
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bint spacy
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int tag
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int idx
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int lemma
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int sense
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int head
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int dep
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bint sent_start
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uint32_t l_kids
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uint32_t r_kids
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uint32_t l_edge
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uint32_t r_edge
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int ent_iob
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int ent_type # TODO: Is there a better way to do this? Multiple sources of truth..
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hash_t ent_id
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p
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| The token owns all of its linguistic annotations, and holds a const
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| pointer to a #[code LexemeC] struct. The #[code LexemeC] struct owns all
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| of the #[em vocabulary] data about the word — all the dictionary
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| definition stuff that we want to be shared by all instances of the type.
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| Here's the #[code LexemeC] struct, in its entirety:
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+h(3) #[code cdef struct LexemeC]
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+code.
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cdef struct LexemeC:
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int32_t id
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int32_t orth # Allows the string to be retrieved
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int32_t length # Length of the string
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uint64_t flags # These are the most useful parts.
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int32_t cluster # Distributional similarity cluster
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float prob # Probability
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float sentiment # Slot for sentiment
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int32_t lang
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int32_t lower # These string views made sense
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int32_t norm # when NLP meant linear models.
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int32_t shape # Now they're less relevant, and
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int32_t prefix # will probably be revised.
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int32_t suffix
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float* vector # <-- This was a design mistake, and will change.
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+h(2, "dynamic-views") Dynamic views
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+h(3) Text
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p
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| You might have noticed that in all of the structs above, there's not a
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| string to be found. The strings are all stored separately, in the
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| #[+api("stringstore") #[code StringStore]] class. The lexemes don't know
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| the strings — they only know their integer IDs. The document string is
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| never stored anywhere, either. Instead, it's reconstructed by iterating
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| over the tokens, which look up the #[code orth] attribute of their
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| underlying lexeme. Once we have the orth ID, we can fetch the string
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| from the vocabulary. Finally, each token knows whether a single
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| whitespace character (#[code ' ']) should be used to separate it from
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| the subsequent tokens. This allows us to preserve whitespace.
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+code.
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cdef print_text(Vocab vocab, const TokenC* tokens, int length):
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for i in range(length):
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word_string = vocab.strings[tokens.lex.orth]
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if tokens.lex.spacy:
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word_string += ' '
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print(word_string)
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p
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| This is why you get whitespace tokens in spaCy — we need those tokens,
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| so that we can reconstruct the document string. I also think you should
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| have those tokens anyway. Most NLP libraries strip them, making it very
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| difficult to recover the paragraph information once you're at the token
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| level. You'll never have that sort of problem with spaCy — because
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| there's a single source of truth.
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+h(3) #[code cdef class Token]
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p When you do...
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+code.
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doc[i]
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p
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| ...you get back an instance of class #[code spacy.tokens.token.Token].
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| This instance owns no data. Instead, it holds the information
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| #[code (doc, i)], and uses these to retrieve all information via the
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| parent container.
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+h(3) #[code cdef class Span]
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p When you do...
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+code.
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doc[i : j]
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p
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| ...you get back an instance of class #[code spacy.tokens.span.Span].
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| #[code Span] instances are also returned by the #[code .sents],
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| #[code .ents] and #[code .noun_chunks] iterators of the #[code Doc]
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| object. A #[code Span] is a slice of tokens, with an optional label
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| attached. Its data model is:
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+code.
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cdef class Span:
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cdef readonly Doc doc
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cdef int start
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cdef int end
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cdef int start_char
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cdef int end_char
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cdef int label
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p
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| Once again, the #[code Span] owns almost no data. Instead, it refers
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| back to the parent #[code Doc] container.
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p
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| The #[code start] and #[code end] attributes refer to token positions,
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| while #[code start_char] and #[code end_char] record the character
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| positions of the span. By recording the character offsets, we can still
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| use the #[code Span] object if the tokenization of the document changes.
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+h(3) #[code cdef class Lexeme]
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p When you do...
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+code.
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vocab[u'the']
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p
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| ...you get back an instance of class #[code spacy.lexeme.Lexeme]. The
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| #[code Lexeme]'s data model is:
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+code.
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cdef class Lexeme:
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cdef LexemeC* c
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cdef readonly Vocab vocab
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