mirror of https://github.com/explosion/spaCy.git
299 lines
6.7 KiB
Cython
299 lines
6.7 KiB
Cython
"""
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Fill an array, context, with every _atomic_ value our features reference.
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We then write the _actual features_ as tuples of the atoms. The machinery
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that translates from the tuples to feature-extractors (which pick the values
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out of "context") is in features/extractor.pyx
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The atomic feature names are listed in a big enum, so that the feature tuples
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can refer to them.
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"""
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from libc.string cimport memset
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from itertools import combinations
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from ..tokens cimport TokenC
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from ._state cimport State
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from ._state cimport get_s2, get_s1, get_s0, get_n0, get_n1, get_n2
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from ._state cimport get_p2, get_p1
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from ._state cimport get_e0, get_e1
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from ._state cimport has_head, get_left, get_right
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from ._state cimport count_left_kids, count_right_kids
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cdef inline void fill_token(atom_t* context, const TokenC* token) nogil:
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if token is NULL:
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context[0] = 0
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context[1] = 0
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context[2] = 0
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context[3] = 0
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context[4] = 0
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context[5] = 0
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context[6] = 0
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else:
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context[0] = token.lex.orth
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context[1] = token.lemma
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context[2] = token.tag
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context[3] = token.lex.cluster
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# We've read in the string little-endian, so now we can take & (2**n)-1
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# to get the first n bits of the cluster.
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# e.g. s = "1110010101"
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# s = ''.join(reversed(s))
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# first_4_bits = int(s, 2)
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# print first_4_bits
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# 5
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# print "{0:b}".format(prefix).ljust(4, '0')
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# 1110
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# What we're doing here is picking a number where all bits are 1, e.g.
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# 15 is 1111, 63 is 111111 and doing bitwise AND, so getting all bits in
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# the source that are set to 1.
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context[4] = token.lex.cluster & 63
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context[5] = token.lex.cluster & 15
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context[6] = token.dep if has_head(token) else 0
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context[7] = token.lex.prefix
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context[8] = token.lex.suffix
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context[9] = token.lex.shape
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cdef int fill_context(atom_t* context, State* state) except -1:
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# Take care to fill every element of context!
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# We could memset, but this makes it very easy to have broken features that
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# make almost no impact on accuracy. If instead they're unset, the impact
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# tends to be dramatic, so we get an obvious regression to fix...
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fill_token(&context[S2w], get_s2(state))
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fill_token(&context[S1w], get_s1(state))
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fill_token(&context[S1rw], get_right(state, get_s1(state), 1))
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fill_token(&context[S0lw], get_left(state, get_s0(state), 1))
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fill_token(&context[S0l2w], get_left(state, get_s0(state), 2))
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fill_token(&context[S0w], get_s0(state))
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fill_token(&context[S0r2w], get_right(state, get_s0(state), 2))
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fill_token(&context[S0rw], get_right(state, get_s0(state), 1))
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fill_token(&context[N0lw], get_left(state, get_n0(state), 1))
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fill_token(&context[N0l2w], get_left(state, get_n0(state), 2))
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fill_token(&context[N0w], get_n0(state))
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fill_token(&context[N1w], get_n1(state))
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fill_token(&context[N2w], get_n2(state))
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fill_token(&context[P1w], get_p1(state))
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fill_token(&context[P2w], get_p2(state))
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fill_token(&context[E0w], get_e0(state))
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fill_token(&context[E1w], get_e1(state))
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if state.stack_len >= 1:
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context[dist] = state.stack[0] - state.i
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else:
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context[dist] = 0
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context[N0lv] = max(count_left_kids(get_n0(state)), 5)
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context[S0lv] = max(count_left_kids(get_s0(state)), 5)
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context[S0rv] = max(count_right_kids(get_s0(state)), 5)
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context[S1lv] = max(count_left_kids(get_s1(state)), 5)
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context[S1rv] = max(count_right_kids(get_s1(state)), 5)
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context[S0_has_head] = 0
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context[S1_has_head] = 0
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context[S2_has_head] = 0
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if state.stack_len >= 1:
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context[S0_has_head] = has_head(get_s0(state)) + 1
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if state.stack_len >= 2:
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context[S1_has_head] = has_head(get_s1(state)) + 1
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if state.stack_len >= 3:
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context[S2_has_head] = has_head(get_s2(state))
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ner = (
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(N0W,),
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(P1W,),
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(N1W,),
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(P2W,),
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(N2W,),
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(P1W, N0W,),
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(N0W, N1W),
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(N0_prefix,),
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(N0_suffix,),
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(P1_shape,),
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(N0_shape,),
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(N1_shape,),
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(P1_shape, N0_shape,),
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(N0_shape, P1_shape,),
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(P1_shape, N0_shape, N1_shape),
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(N2_shape,),
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(P2_shape,),
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#(P2_norm, P1_norm, W_norm),
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#(P1_norm, W_norm, N1_norm),
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#(W_norm, N1_norm, N2_norm)
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(P2p,),
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(P1p,),
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(N0p,),
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(N1p,),
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(N2p,),
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(P1p, N0p),
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(N0p, N1p),
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(P2p, P1p, N0p),
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(P1p, N0p, N1p),
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(N0p, N1p, N2p),
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(P2c,),
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(P1c,),
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(N0c,),
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(N1c,),
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(N2c,),
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(P1c, N0c),
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(N0c, N1c),
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(E0W,),
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(E0c,),
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(E0p,),
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(E0W, N0W),
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(E0c, N0W),
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(E0p, N0W),
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(E0p, P1p, N0p),
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(E0c, P1c, N0c),
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(E0w, P1c),
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(E0p, P1p),
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(E0c, P1c),
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(E0p, E1p),
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(E0c, P1p),
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(E1W,),
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(E1c,),
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(E1p,),
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(E0W, E1W),
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(E0W, E1p,),
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(E0p, E1W,),
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(E0p, E1W),
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)
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unigrams = (
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(S2W, S2p),
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(S2c6, S2p),
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(S1W, S1p),
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(S1c6, S1p),
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(S0W, S0p),
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(S0c6, S0p),
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(N0W, N0p),
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(N0p,),
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(N0c,),
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(N0c6, N0p),
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(N0L,),
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(N1W, N1p),
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(N1c6, N1p),
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(N2W, N2p),
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(N2c6, N2p),
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(S0r2W, S0r2p),
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(S0r2c6, S0r2p),
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(S0r2L,),
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(S0rW, S0rp),
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(S0rc6, S0rp),
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(S0rL,),
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(S0l2W, S0l2p),
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(S0l2c6, S0l2p),
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(S0l2L,),
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(S0lW, S0lp),
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(S0lc6, S0lp),
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(S0lL,),
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(N0l2W, N0l2p),
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(N0l2c6, N0l2p),
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(N0l2L,),
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(N0lW, N0lp),
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(N0lc6, N0lp),
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(N0lL,),
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)
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s0_n0 = (
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(S0W, S0p, N0W, N0p),
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(S0c, S0p, N0c, N0p),
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(S0c6, S0p, N0c6, N0p),
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(S0c4, S0p, N0c4, N0p),
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(S0p, N0p),
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(S0W, N0p),
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(S0p, N0W),
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(S0W, N0c),
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(S0c, N0W),
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(S0p, N0c),
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(S0c, N0p),
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(S0W, S0rp, N0p),
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(S0p, S0rp, N0p),
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(S0p, N0lp, N0W),
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(S0p, N0lp, N0p),
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)
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s1_n0 = (
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(S1p, N0p),
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(S1c, N0c),
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(S1c, N0p),
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(S1p, N0c),
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(S1W, S1p, N0p),
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(S1p, N0W, N0p),
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(S1c6, S1p, N0c6, N0p),
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)
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s0_n1 = (
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(S0p, N1p),
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(S0c, N1c),
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(S0c, N1p),
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(S0p, N1c),
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(S0W, S0p, N1p),
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(S0p, N1W, N1p),
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(S0c6, S0p, N1c6, N1p),
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)
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n0_n1 = (
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(N0W, N0p, N1W, N1p),
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(N0W, N0p, N1p),
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(N0p, N1W, N1p),
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(N0c, N0p, N1c, N1p),
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(N0c6, N0p, N1c6, N1p),
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(N0c, N1c),
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(N0p, N1c),
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)
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tree_shape = (
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(dist,),
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(S0p, S0_has_head, S1_has_head, S2_has_head),
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(S0p, S0lv, S0rv),
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(N0p, N0lv),
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)
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trigrams = (
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(N0p, N1p, N2p),
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(S0p, S0lp, S0l2p),
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(S0p, S0rp, S0r2p),
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(S0p, S1p, S2p),
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(S1p, S0p, N0p),
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(S0p, S0lp, N0p),
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(S0p, N0p, N0lp),
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(N0p, N0lp, N0l2p),
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(S0W, S0p, S0rL, S0r2L),
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(S0p, S0rL, S0r2L),
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(S0W, S0p, S0lL, S0l2L),
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(S0p, S0lL, S0l2L),
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(N0W, N0p, N0lL, N0l2L),
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(N0p, N0lL, N0l2L),
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)
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