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spaCy/spacy/syntax/_parse_features.pyx

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"""
Fill an array, context, with every _atomic_ value our features reference.
We then write the _actual features_ as tuples of the atoms. The machinery
that translates from the tuples to feature-extractors (which pick the values
out of "context") is in features/extractor.pyx
The atomic feature names are listed in a big enum, so that the feature tuples
can refer to them.
"""
from itertools import combinations
from ..tokens cimport TokenC
from ._state cimport State
from ._state cimport get_s2, get_s1, get_s0, get_n0, get_n1, get_n2
from ._state cimport get_p2, get_p1
from ._state cimport get_e0, get_e1
from ._state cimport has_head, get_left, get_right
from ._state cimport count_left_kids, count_right_kids
cdef inline void fill_token(atom_t* context, const TokenC* token) nogil:
if token is NULL:
context[0] = 0
context[1] = 0
context[2] = 0
context[3] = 0
context[4] = 0
context[5] = 0
context[6] = 0
else:
context[0] = token.lex.orth
context[1] = token.lemma
context[2] = token.tag
context[3] = token.lex.cluster
# We've read in the string little-endian, so now we can take & (2**n)-1
# to get the first n bits of the cluster.
# e.g. s = "1110010101"
# s = ''.join(reversed(s))
# first_4_bits = int(s, 2)
# print first_4_bits
# 5
# print "{0:b}".format(prefix).ljust(4, '0')
# 1110
# What we're doing here is picking a number where all bits are 1, e.g.
# 15 is 1111, 63 is 111111 and doing bitwise AND, so getting all bits in
# the source that are set to 1.
context[4] = token.lex.cluster & 63
context[5] = token.lex.cluster & 15
context[6] = token.dep if has_head(token) else 0
context[7] = token.lex.prefix
context[8] = token.lex.suffix
context[9] = token.lex.shape
cdef int fill_context(atom_t* context, State* state) except -1:
# This fills in the basic properties of each of our "slot" tokens, e.g.
# word on top of the stack, word at the front of the buffer, etc.
fill_token(&context[S2w], get_s2(state))
fill_token(&context[S1w], get_s1(state))
fill_token(&context[S1rw], get_right(state, get_s1(state), 1))
fill_token(&context[S0lw], get_left(state, get_s0(state), 1))
fill_token(&context[S0l2w], get_left(state, get_s0(state), 2))
fill_token(&context[S0w], get_s0(state))
fill_token(&context[S0r2w], get_right(state, get_s0(state), 2))
fill_token(&context[S0rw], get_right(state, get_s0(state), 1))
fill_token(&context[N0lw], get_left(state, get_n0(state), 1))
fill_token(&context[N0l2w], get_left(state, get_n0(state), 2))
fill_token(&context[N0w], get_n0(state))
fill_token(&context[N1w], get_n1(state))
fill_token(&context[P1w], get_p1(state))
fill_token(&context[P2w], get_p2(state))
fill_token(&context[E0w], get_e0(state))
fill_token(&context[E1w], get_e1(state))
if state.stack_len >= 1:
context[dist] = state.stack[0] - state.i
else:
context[dist] = 0
context[N0lv] = max(count_left_kids(get_n0(state)), 5)
context[S0lv] = max(count_left_kids(get_s0(state)), 5)
context[S0rv] = max(count_right_kids(get_s0(state)), 5)
context[S1lv] = max(count_left_kids(get_s1(state)), 5)
context[S1rv] = max(count_right_kids(get_s1(state)), 5)
context[S0_has_head] = 0
context[S1_has_head] = 0
context[S2_has_head] = 0
if state.stack_len >= 1:
context[S0_has_head] = has_head(get_s0(state)) + 1
if state.stack_len >= 2:
context[S1_has_head] = has_head(get_s1(state)) + 1
if state.stack_len >= 3:
context[S2_has_head] = has_head(get_s2(state))
ner = (
(N0w,),
(P1w,),
(N1w,),
(P2w,),
(N2w,),
(P1w, N0w,),
(N0w, N1w),
(N0_prefix,),
(N0_suffix,),
(P1_shape,),
(N0_shape,),
(N1_shape,),
(P1_shape, N0_shape,),
(N0_shape, P1_shape,),
(P1_shape, N0_shape, N1_shape),
(N2_shape,),
(P2_shape,),
#(P2_norm, P1_norm, W_norm),
#(P1_norm, W_norm, N1_norm),
#(W_norm, N1_norm, N2_norm)
(P2p,),
(P1p,),
(N0p,),
(N1p,),
(N2p,),
(P1p, N0p),
(N0p, N1p),
(P2p, P1p, N0p),
(P1p, N0p, N1p),
(N0p, N1p, N2p),
(P2c,),
(P1c,),
(N0c,),
(N1c,),
(N2c,),
(P1c, N0c),
(N0c, N1c),
(E0w,),
(E0c,),
(E0p,),
(E0w, N0w),
(E0c, N0w),
(E0p, N0w),
(E0p, P1p, N0p),
(E0c, P1c, N0c),
(E0w, P1c),
(E0p, P1p),
(E0c, P1c),
(E0p, E1p),
(E0c, P1p),
(E1w,),
(E1c,),
(E1p,),
(E0w, E1w),
(E0w, E1p,),
(E0p, E1w,),
(E0p, E1w),
)
unigrams = (
(S2W, S2p),
(S2c6, S2p),
(S1W, S1p),
(S1c6, S1p),
(S0W, S0p),
(S0c6, S0p),
(N0W, N0p),
(N0p,),
(N0c,),
(N0c6, N0p),
(N0L,),
(N1W, N1p),
(N1c6, N1p),
(N2W, N2p),
(N2c6, N2p),
(S0r2W, S0r2p),
(S0r2c6, S0r2p),
(S0r2L,),
(S0rW, S0rp),
(S0rc6, S0rp),
(S0rL,),
(S0l2W, S0l2p),
(S0l2c6, S0l2p),
(S0l2L,),
(S0lW, S0lp),
(S0lc6, S0lp),
(S0lL,),
(N0l2W, N0l2p),
(N0l2c6, N0l2p),
(N0l2L,),
(N0lW, N0lp),
(N0lc6, N0lp),
(N0lL,),
)
s0_n0 = (
(S0W, S0p, N0W, N0p),
(S0c, S0p, N0c, N0p),
(S0c6, S0p, N0c6, N0p),
(S0c4, S0p, N0c4, N0p),
(S0p, N0p),
(S0W, N0p),
(S0p, N0W),
(S0W, N0c),
(S0c, N0W),
(S0p, N0c),
(S0c, N0p),
(S0W, S0rp, N0p),
(S0p, S0rp, N0p),
(S0p, N0lp, N0W),
(S0p, N0lp, N0p),
)
s1_n0 = (
(S1p, N0p),
(S1c, N0c),
(S1c, N0p),
(S1p, N0c),
(S1W, S1p, N0p),
(S1p, N0W, N0p),
(S1c6, S1p, N0c6, N0p),
)
s0_n1 = (
(S0p, N1p),
(S0c, N1c),
(S0c, N1p),
(S0p, N1c),
(S0W, S0p, N1p),
(S0p, N1W, N1p),
(S0c6, S0p, N1c6, N1p),
)
n0_n1 = (
(N0W, N0p, N1W, N1p),
(N0W, N0p, N1p),
(N0p, N1W, N1p),
(N0c, N0p, N1c, N1p),
(N0c6, N0p, N1c6, N1p),
(N0c, N1c),
(N0p, N1c),
)
tree_shape = (
(dist,),
(S0p, S0_has_head, S1_has_head, S2_has_head),
(S0p, S0lv, S0rv),
(N0p, N0lv),
)
trigrams = (
(N0p, N1p, N2p),
(S0p, S0lp, S0l2p),
(S0p, S0rp, S0r2p),
(S0p, S1p, S2p),
(S1p, S0p, N0p),
(S0p, S0lp, N0p),
(S0p, N0p, N0lp),
(N0p, N0lp, N0l2p),
(S0W, S0p, S0rL, S0r2L),
(S0p, S0rL, S0r2L),
(S0W, S0p, S0lL, S0l2L),
(S0p, S0lL, S0l2L),
(N0W, N0p, N0lL, N0l2L),
(N0p, N0lL, N0l2L),
)
arc_eager = (
(S0w, S0p),
(S0w,),
(S0p,),
(N0w, N0p),
(N0w,),
(N0p,),
(N1w, N1p),
(N1w,),
(N1p,),
(N2w, N2p),
(N2w,),
(N2p,),
(S0w, S0p, N0w, N0p),
(S0w, S0p, N0w),
(S0w, N0w, N0p),
(S0w, S0p, N0p),
(S0p, N0w, N0p),
(S0w, N0w),
(S0p, N0p),
(N0p, N1p),
(N0p, N1p, N2p),
(S0p, N0p, N1p),
(S1p, S0p, N0p),
(S0p, S0lp, N0p),
(S0p, S0rp, N0p),
(S0p, N0p, N0lp),
(dist, S0w),
(dist, S0p),
(dist, N0w),
(dist, N0p),
(dist, S0w, N0w),
(dist, S0p, N0p),
(S0w, S0rv),
(S0p, S0rv),
(S0w, S0lv),
(S0p, S0lv),
(N0w, N0lv),
(N0p, N0lv),
(S1w,),
(S1p,),
(S0lw,),
(S0lp,),
(S0rw,),
(S0rp,),
(N0lw,),
(N0lp,),
(S2w,),
(S2p,),
(S0l2w,),
(S0l2p,),
(S0r2w,),
(S0r2p,),
(N0l2w,),
(N0l2p,),
(S0p, S0lp, S0l2p),
(S0p, S0rp, S0r2p),
(S0p, S1p, S2p),
(N0p, N0lp, N0l2p),
(S0L,),
(S0lL,),
(S0rL,),
(N0lL,),
(S1L,),
(S0l2L,),
(S0r2L,),
(N0l2L,),
(S0w, S0rL, S0r2L),
(S0p, S0rL, S0r2L),
(S0w, S0lL, S0l2L),
(S0p, S0lL, S0l2L),
(N0w, N0lL, N0l2L),
(N0p, N0lL, N0l2L),
)
label_sets = (
(S0w, S0lL, S0l2L),
(S0p, S0rL, S0r2L),
(S0p, S0lL, S0l2L),
(S0p, S0rL, S0r2L),
(N0w, N0lL, N0l2L),
(N0p, N0lL, N0l2L),
)
extra_labels = (
(S0p, S0lL, S0lp),
(S0p, S0lL, S0l2L),
(S0p, S0rL, S0rp),
(S0p, S0rL, S0r2L),
(S0p, S0lL, S0rL),
(S1p, S0L, S0rL),
(S1p, S0L, S0lL),
)
# Koo et al (2008) dependency features, using Brown clusters.
clusters = (
# Koo et al have (head, child) --- we have S0, N0 for both.
(S0c4, N0c4),
(S0c6, N0c6),
(S0c, N0c),
(S0p, N0c4),
(S0p, N0c6),
(S0p, N0c),
(S0c4, N0p),
(S0c6, N0p),
(S0c, N0p),
# Siblings --- right arc
(S0c4, S0rc4, N0c4),
(S0c6, S0rc6, N0c6),
(S0p, S0rc4, N0c4),
(S0c4, S0rp, N0c4),
(S0c4, S0rc4, N0p),
# Siblings --- left arc
(S0c4, N0lc4, N0c4),
(S0c6, N0c6, N0c6),
(S0c4, N0lc4, N0p),
(S0c4, N0lp, N0c4),
(S0p, N0lc4, N0c4),
# Grand-child, right-arc
(S1c4, S0c4, N0c4),
(S1c6, S0c6, N0c6),
(S1p, S0c4, N0c4),
(S1c4, S0p, N0c4),
(S1c4, S0c4, N0p),
# Grand-child, left-arc
(S0lc4, S0c4, N0c4),
(S0lc6, S0c6, N0c6),
(S0lp, S0c4, N0c4),
(S0lc4, S0p, N0c4),
(S0lc4, S0c4, N0p)
)
hasty = s0_n0 + n0_n1 + trigrams
def pos_bigrams():
kernels = [S2w, S1w, S0w, S0lw, S0rw, N0w, N0lw, N1w]
bitags = []
for t1, t2 in combinations(kernels, 2):
feat = (t1 + 1, t2 + 1)
bitags.append(feat)
print "Adding %d bitags" % len(bitags)
return tuple(bitags)
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