mirror of https://github.com/explosion/spaCy.git
190 lines
6.7 KiB
Cython
190 lines
6.7 KiB
Cython
# cython: profile=True
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# cython: embedsignature=True
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"""Common classes and utilities across languages.
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Provides the main implementation for the spacy tokenizer. Specific languages
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subclass the Language class, over-writing the tokenization rules as necessary.
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Special-case tokenization rules are read from data/<lang>/tokenization .
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"""
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from __future__ import unicode_literals
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from libc.stdlib cimport calloc, free
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import json
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from os import path
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from .util import read_lang_data
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from spacy.tokens import Tokens
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cdef class Language:
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"""Base class for language-specific tokenizers.
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Most subclasses will override the _split or _split_one methods, which take
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a string of non-whitespace characters and output a list of strings. This
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function is called by _tokenize, which sits behind a cache and turns the
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list of strings into Lexeme objects via the Lexicon. Most languages will not
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need to override _tokenize or tokenize.
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The language is supplied a list of boolean functions, used to compute flag
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features. These are passed to the language's Lexicon object.
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The language's name is used to look up default data-files, found in data/<name.
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"""
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def __cinit__(self, name, string_features, flag_features):
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if flag_features is None:
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flag_features = []
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if string_features is None:
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string_features = []
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self.name = name
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self.cache = {}
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lang_data = read_lang_data(name)
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rules, words, probs, clusters, case_stats, tag_stats = lang_data
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self.lexicon = Lexicon(words, probs, clusters, case_stats, tag_stats,
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string_features, flag_features)
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self._load_special_tokenization(rules)
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self.tokens_class = Tokens
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property nr_types:
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def __get__(self):
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"""Return the number of lexical types in the vocabulary"""
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return self.lexicon.size
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cpdef Lexeme lookup(self, unicode string):
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"""Retrieve (or create, if not found) a Lexeme for a string, and return it.
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Args:
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string (unicode): The string to be looked up. Must be unicode, not bytes.
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Returns:
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lexeme (Lexeme): A reference to a lexical type.
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"""
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return self.lexicon.lookup(string)
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cpdef list tokenize(self, unicode string):
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"""Tokenize a string.
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The tokenization rules are defined in two places:
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* The data/<lang>/tokenization table, which handles special cases like contractions;
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* The appropriate :py:meth:`find_split` function, which is used to split
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off punctuation etc.
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Args:
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string (unicode): The string to be tokenized.
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Returns:
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tokens (Tokens): A Tokens object, giving access to a sequence of LexIDs.
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"""
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assert string
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cdef size_t length = len(string)
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cdef size_t start = 0
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cdef size_t i = 0
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cdef Tokens tokens = self.tokens_class()
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for c in string:
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if c == ' ':
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if start < i:
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self._tokenize(tokens, string[start:i])
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start = i + 1
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i += 1
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if start < i:
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self._tokenize(tokens, string[start:i])
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assert tokens
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return tokens.lexemes
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cdef _tokenize(self, Tokens tokens, unicode string):
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cdef list lexemes
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if len(string) == 1:
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lexemes = [self.lookup(string)]
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elif string in self.cache:
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lexemes = self.cache[string]
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else:
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lexemes = []
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substrings = self._split(string)
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for i, substring in enumerate(substrings):
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lexemes.append(self.lexicon.lookup(substring))
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self.cache[string] = lexemes
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cdef Lexeme lexeme
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for lexeme in lexemes:
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tokens.append(lexeme)
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cdef list _split(self, unicode string):
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"""Find how to split a contiguous span of non-space characters into substrings.
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This method calls find_split repeatedly. Most languages will want to
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override _split_one, but it may be useful to override this instead.
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Args:
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chunk (unicode): The string to be split, e.g. u"Mike's!"
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Returns:
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substrings (list): The component substrings, e.g. [u"Mike", "'s", "!"].
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"""
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substrings = []
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while string:
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split = self._split_one(string)
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if split == 0:
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substrings.append(string)
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break
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substrings.append(string[:split])
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string = string[split:]
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return substrings
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cdef int _split_one(self, unicode word):
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return len(word)
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def _load_special_tokenization(self, token_rules):
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'''Load special-case tokenization rules.
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Loads special-case tokenization rules into the Language.cache cache,
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read from data/<lang>/tokenization . The special cases are loaded before
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any language data is tokenized, giving these priority. For instance,
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the English tokenization rules map "ain't" to ["are", "not"].
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Args:
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token_rules (list): A list of (chunk, tokens) pairs, where chunk is
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a string and tokens is a list of strings.
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'''
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for string, substrings in token_rules:
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lexemes = []
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for i, substring in enumerate(substrings):
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lexemes.append(self.lexicon.lookup(substring))
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self.cache[string] = lexemes
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cdef class Lexicon:
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def __cinit__(self, words, probs, clusters, case_stats, tag_stats,
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string_features, flag_features):
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self._flag_features = flag_features
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self._string_features = string_features
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self._dict = {}
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self.size = 0
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cdef Lexeme word
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for string in words:
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word = Lexeme(string, probs.get(string, 0.0), clusters.get(string, 0),
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case_stats.get(string, {}), tag_stats.get(string, {}),
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self._string_features, self._flag_features)
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self._dict[string] = word
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self.size += 1
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cpdef Lexeme lookup(self, unicode string):
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"""Retrieve (or create, if not found) a Lexeme for a string, and return it.
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Args
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string (unicode): The string to be looked up. Must be unicode, not bytes.
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Returns:
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lexeme (Lexeme): A reference to a lexical type.
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"""
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assert len(string) != 0
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if string in self._dict:
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return self._dict[string]
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cdef Lexeme word = Lexeme(string, 0, 0, {}, {}, self._string_features,
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self._flag_features)
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self._dict[string] = word
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self.size += 1
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return word
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