spaCy/spacy/structs.pxd

114 lines
2.4 KiB
Cython

from libc.stdint cimport int32_t, int64_t, uint8_t, uint32_t, uint64_t
from libcpp.unordered_map cimport unordered_map
from libcpp.unordered_set cimport unordered_set
from libcpp.vector cimport vector
from .parts_of_speech cimport univ_pos_t
from .typedefs cimport attr_t, flags_t, hash_t
cdef struct LexemeC:
flags_t flags
attr_t lang
attr_t id
attr_t length
attr_t orth
attr_t lower
attr_t norm
attr_t shape
attr_t prefix
attr_t suffix
cdef struct SpanC:
hash_t id
int start
int end
int start_char
int end_char
attr_t label
attr_t kb_id
cdef struct TokenC:
const LexemeC* lex
uint64_t morph
univ_pos_t pos
bint spacy
attr_t tag
int idx
attr_t lemma
attr_t norm
int head
attr_t dep
uint32_t l_kids
uint32_t r_kids
uint32_t l_edge
uint32_t r_edge
int sent_start
int ent_iob
attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth..
attr_t ent_kb_id
hash_t ent_id
cdef struct MorphAnalysisC:
hash_t key
int length
attr_t* fields
attr_t* features
# Internal struct, for storage and disambiguation of entities.
cdef struct KBEntryC:
# The hash of this entry's unique ID/name in the kB
hash_t entity_hash
# Allows retrieval of the entity vector, as an index into a vectors table of the KB.
# Can be expanded later to refer to multiple rows (compositional model to reduce storage footprint).
int32_t vector_index
# Allows retrieval of a struct of non-vector features.
# This is currently not implemented and set to -1 for the common case where there are no features.
int32_t feats_row
# log probability of entity, based on corpus frequency
float freq
# Each alias struct stores a list of Entry pointers with their prior probabilities
# for this specific mention/alias.
cdef struct AliasC:
# All entry candidates for this alias
vector[int64_t] entry_indices
# Prior probability P(entity|alias) - should sum up to (at most) 1.
vector[float] probs
cdef struct EdgeC:
hash_t label
int32_t head
int32_t tail
cdef struct GraphC:
vector[vector[int32_t]] nodes
vector[EdgeC] edges
vector[float] weights
vector[int] n_heads
vector[int] n_tails
vector[int] first_head
vector[int] first_tail
unordered_set[int]* roots
unordered_map[hash_t, int]* node_map
unordered_map[hash_t, int]* edge_map