spaCy/spacy/kb.pxd

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2019-03-15 10:17:35 +00:00
"""Knowledge-base for entity or concept linking."""
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from libcpp.vector cimport vector
from libc.stdint cimport int32_t
from spacy.typedefs cimport attr_t
# Internal struct, for storage and disambiguation. This isn't what we return
# to the user as the answer to "here's your entity". It's the minimum number
# of bits we need to keep track of the answers.
cdef struct _EntryC:
# Allows retrieval of one or more vectors.
# Each element of vector_rows should be an index into a vectors table.
# Every entry should have the same number of vectors, so we can avoid storing
# the number of vectors in each knowledge-base struct
const int32_t* vector_rows
# Allows retrieval of a struct of non-vector features. We could make this a
# pointer, but we have 32 bits left over in the struct after prob, so we'd
# like this to only be 32 bits. We can also set this to -1, for the common
# case where there are no features.
int32_t feats_row
float prob # log probability of entity, based on corpus frequency
cdef class KnowledgeBase:
cdef Pool mem
# This maps 64bit keys to 64bit values. Here the key would be a hash of
# a unique string name for the entity, and the value would be the position
# of the _EntryC struct in our vector.
# The PreshMap is pretty space efficient, as it uses open addressing. So
# the only overhead is the vacancy rate, which is approximately 30%.
cdef PreshMap _index
# Each entry takes 128 bits, and again we'll have a 30% or so overhead for
# over allocation.
# In total we end up with (N*128*1.3)+(N*128*1.3) bits for N entries.
# Storing 1m entries would take 41.6mb under this scheme.
cdef vector[_EntryC] _entries
# This is the part which might take more space: storing various
# categorical features for the entries, and storing vectors for disambiguation
# and possibly usage.
# If each entry gets a 300-dimensional vector, for 1m entries we would need
# 1.2gb. That gets expensive fast. What might be better is to avoid learning
# a unique vector for every entity. We could instead have a compositional
# model, that embeds different features of the entities into vectors. We'll
# still want some per-entity features, like the Wikipedia text or entity
# co-occurrence. Hopefully those vectors can be narrow, e.g. 64 dimensions.
cdef object _vectors_table
# It's very useful to track categorical features, at least for output, even
# if they're not useful in the model itself. For instance, we should be
# able to track stuff like a person's date of birth or whatever. This can
# easily make the KB bigger, but if this isn't needed by the model, and it's
# optional data, we can let users configure a DB as the backend for this.
cdef object _features_table
# This should map mention hashes to (entry_id, prob) tuples. The probability
# should be P(entity | mention), which is pretty important to know.
# We can pack both pieces of information into a 64-bit vale, to keep things
# efficient.
cdef object _aliases_table
def __len__(self):
return self._entries.size()
def add(self, name, float prob, vectors=None, features=None, aliases=None):
if name in self:
return
cdef attr_t orth = get_string_name(name)
self.c_add(orth, prob, self._vectors_table.get_pointer(vectors),
self._features_table.get(features))
for alias in aliases:
self._aliases_table.add(alias, orth)
cdef void c_add(self, attr_t orth, float prob, const int32_t* vector_rows,
int feats_row) nogil:
"""Add an entry to the knowledge base."""
# This is what we'll map the orth to. It's where the entry will sit
# in the vector of entries, so we can get it later.
cdef int64_t index = self.c.size()
self._entries.push_back(
_EntryC(
vector_rows=vector_rows,
feats_row=feats_row,
prob=prob
))
self._index[orth] = index
return index