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103 lines
3.9 KiB
Cython
103 lines
3.9 KiB
Cython
# cython: profile=True
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# cython: embedsignature=True
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from libc.stdlib cimport calloc, free, realloc
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cdef class Lexeme:
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"""A lexical type --- a word, punctuation symbol, whitespace sequence, etc
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keyed by a case-sensitive unicode string. All tokens with the same string,
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e.g. all instances of "dog", ",", "NASA" etc should be mapped to the same
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Lexeme.
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You should avoid instantiating Lexemes directly, and instead use the
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:py:meth:`space.lang.Language.tokenize` and :py:meth:`spacy.lang.Language.lookup`
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methods on the global object exposed by the language you're working with,
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e.g. :py:data:`spacy.en.EN`.
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Attributes:
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string (unicode):
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The unicode string.
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Implemented as a property; relatively expensive.
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length (size_t):
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The number of unicode code-points in the string.
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prob (double):
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An estimate of the word's unigram log probability.
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Probabilities are calculated from a large text corpus, and smoothed using
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simple Good-Turing. Estimates are read from data/en/probabilities, and
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can be replaced using spacy.en.load_probabilities.
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cluster (size_t):
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An integer representation of the word's Brown cluster.
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A Brown cluster is an address into a binary tree, which gives some (noisy)
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information about the word's distributional context.
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>>> strings = (u'pineapple', u'apple', u'dapple', u'scalable')
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>>> print ["{0:b"} % lookup(s).cluster for s in strings]
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["100111110110", "100111100100", "01010111011001", "100111110110"]
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The clusterings are unideal, but often slightly useful.
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"pineapple" and "apple" share a long prefix, indicating a similar meaning,
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while "dapple" is totally different. On the other hand, "scalable" receives
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the same cluster ID as "pineapple", which is not what we'd like.
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"""
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def __cinit__(self, unicode string, double prob, int cluster, dict case_stats,
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dict tag_stats, list string_features, list flag_features):
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self.prob = prob
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self.cluster = cluster
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self.length = len(string)
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self.string = string
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self.views = []
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for string_feature in string_features:
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view = string_feature(string, prob, cluster, case_stats, tag_stats)
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self.views.append(view)
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for i, flag_feature in enumerate(flag_features):
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if flag_feature(string, prob, case_stats, tag_stats):
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self.flags |= (1 << i)
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def __dealloc__(self):
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pass
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cpdef bint check_flag(self, size_t flag_id) except *:
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"""Lexemes may store language-specific boolean features in a bit-field,
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with values accessed by providing an ID constant to this function.
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The ID constants are exposed as global variables in the language module,
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e.g.
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>>> from spacy.en import EN
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>>> lexeme = EN.lookup(u'Nasa')
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>>> lexeme.check_flag(EN.IS_UPPER)
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False
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>>> lexeme.check_flag(EN.OFT_UPPER)
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True
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"""
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return self.flags & (1 << flag_id)
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cpdef unicode string_view(self, size_t view_id):
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"""Lexemes may store language-specific string-view features, obtained
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by transforming the string, possibly in light of distributional information.
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The string-view features are accessed by providing an ID constant to this
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function.
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The ID constants are exposed as global variables in the language module,
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e.g.
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>>> from spacy.en import EN
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>>> lexeme = EN.lookup(u'Nasa')
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>>> lexeme.string_view(EN.CANON_CASED)
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u'NASA'
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>>> lexeme.string_view(EN.SHAPE)
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u'Xxxx'
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>>> lexeme.string_view(EN.NON_SPARSE)
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u'Xxxx'
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"""
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return self.views[view_id]
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