mirror of https://github.com/explosion/spaCy.git
80 lines
3.0 KiB
Cython
80 lines
3.0 KiB
Cython
# cython: profile=True
|
|
# cython: embedsignature=True
|
|
|
|
|
|
from libc.stdlib cimport calloc, free, realloc
|
|
|
|
cdef class Lexeme:
|
|
"""A lexical type.
|
|
|
|
Clients should avoid instantiating Lexemes directly, and instead use get_lexeme
|
|
from a language module, e.g. spacy.en.get_lexeme . This allows us to use only
|
|
one Lexeme object per lexical type.
|
|
|
|
Attributes:
|
|
id (view_id_t):
|
|
A unique ID of the word's string.
|
|
|
|
Implemented as the memory-address of the string,
|
|
as we use Python's string interning to guarantee that only one copy
|
|
of each string is seen.
|
|
|
|
string (unicode):
|
|
The unicode string.
|
|
|
|
Implemented as a property; relatively expensive.
|
|
|
|
length (size_t):
|
|
The number of unicode code-points in the string.
|
|
|
|
prob (double):
|
|
An estimate of the word's unigram log probability.
|
|
|
|
Probabilities are calculated from a large text corpus, and smoothed using
|
|
simple Good-Turing. Estimates are read from data/en/probabilities, and
|
|
can be replaced using spacy.en.load_probabilities.
|
|
|
|
cluster (int):
|
|
An integer representation of the word's Brown cluster.
|
|
|
|
A Brown cluster is an address into a binary tree, which gives some (noisy)
|
|
information about the word's distributional context.
|
|
|
|
>>> strings = (u'pineapple', u'apple', u'dapple', u'scalable')
|
|
>>> print ["{0:b"} % lookup(s).cluster for s in strings]
|
|
["100111110110", "100111100100", "01010111011001", "100111110110"]
|
|
|
|
The clusterings are unideal, but often slightly useful.
|
|
"pineapple" and "apple" share a long prefix, indicating a similar meaning,
|
|
while "dapple" is totally different. On the other hand, "scalable" receives
|
|
the same cluster ID as "pineapple", which is not what we'd like.
|
|
"""
|
|
def __cinit__(self, unicode string, double prob, int cluster, dict case_stats,
|
|
dict tag_stats, list string_features, list flag_features):
|
|
self.prob = prob
|
|
self.cluster = cluster
|
|
self.length = len(string)
|
|
self.string = string
|
|
|
|
for string_feature in string_features:
|
|
view = string_feature(string, prob, cluster, case_stats, tag_stats)
|
|
self.views.append(view)
|
|
|
|
for i, flag_feature in enumerate(flag_features):
|
|
if flag_feature(string, prob, case_stats, tag_stats):
|
|
self.set_flag(i)
|
|
|
|
def __dealloc__(self):
|
|
pass
|
|
|
|
cpdef bint check_flag(self, size_t flag_id) except *:
|
|
"""Access the value of one of the pre-computed boolean distribution features.
|
|
|
|
Meanings depend on the language-specific distributional features being loaded.
|
|
The suggested features for latin-alphabet languages are: TODO
|
|
"""
|
|
return self.flags & (1 << flag_id)
|
|
|
|
cpdef int set_flag(self, size_t flag_id) except -1:
|
|
self.flags |= (1 << flag_id)
|