mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-24 17:06:29 +03:00
Distinction between outside, missing and blocked NER annotations (#4307)
* remove duplicate unit test * unit test (currently failing) for issue 4267 * bugfix: ensure doc.ents preserves kb_id annotations * fix in setting doc.ents with empty label * rename * test for presetting an entity to a certain type * allow overwriting Outside + blocking presets * fix actions when previous label needs to be kept * fix default ent_iob in set entities * cleaner solution with U- action * remove debugging print statements * unit tests with explicit transitions and is_valid testing * remove U- from move_names explicitly * remove unit tests with pre-trained models that don't work * remove (working) unit tests with pre-trained models * clean up unit tests * move unit tests * small fixes * remove two TODO's from doc.ents comments
This commit is contained in:
Sofie Van Landeghem
Matthew Honnibal
parent
72463b062f
commit
de5a9ecdf3
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@ -118,7 +118,7 @@ class Errors(object):
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E011 = ("Unknown operator: '{op}'. Options: {opts}")
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E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
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E013 = ("Error selecting action in matcher")
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E014 = ("Uknown tag ID: {tag}")
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E014 = ("Unknown tag ID: {tag}")
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E015 = ("Conflicting morphology exception for ({tag}, {orth}). Use "
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"`force=True` to overwrite.")
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E016 = ("MultitaskObjective target should be function or one of: dep, "
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@ -66,7 +66,8 @@ cdef class BiluoPushDown(TransitionSystem):
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UNIT: Counter(),
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OUT: Counter()
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}
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actions[OUT][''] = 1
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actions[OUT][''] = 1 # Represents a token predicted to be outside of any entity
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actions[UNIT][''] = 1 # Represents a token prohibited to be in an entity
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for entity_type in kwargs.get('entity_types', []):
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for action in (BEGIN, IN, LAST, UNIT):
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actions[action][entity_type] = 1
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@ -161,8 +162,7 @@ cdef class BiluoPushDown(TransitionSystem):
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for i in range(self.n_moves):
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if self.c[i].move == move and self.c[i].label == label:
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return self.c[i]
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else:
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raise KeyError(Errors.E022.format(name=name))
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raise KeyError(Errors.E022.format(name=name))
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cdef Transition init_transition(self, int clas, int move, attr_t label) except *:
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# TODO: Apparent Cython bug here when we try to use the Transition()
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@ -266,7 +266,7 @@ cdef class Begin:
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return False
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elif label == 0:
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return False
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elif preset_ent_iob == 1 or preset_ent_iob == 2:
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elif preset_ent_iob == 1:
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# Ensure we don't clobber preset entities. If no entity preset,
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# ent_iob is 0
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return False
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@ -282,8 +282,8 @@ cdef class Begin:
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# Otherwise, force acceptance, even if we're across a sentence
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# boundary or the token is whitespace.
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return True
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elif st.B_(1).ent_iob == 2 or st.B_(1).ent_iob == 3:
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# If the next word is B or O, we can't B now
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elif st.B_(1).ent_iob == 3:
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# If the next word is B, we can't B now
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return False
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elif st.B_(1).sent_start == 1:
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# Don't allow entities to extend across sentence boundaries
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@ -326,6 +326,7 @@ cdef class In:
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@staticmethod
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cdef bint is_valid(const StateC* st, attr_t label) nogil:
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cdef int preset_ent_iob = st.B_(0).ent_iob
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cdef attr_t preset_ent_label = st.B_(0).ent_type
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if label == 0:
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return False
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elif st.E_(0).ent_type != label:
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@ -335,13 +336,22 @@ cdef class In:
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elif st.B(1) == -1:
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# If we're at the end, we can't I.
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return False
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elif preset_ent_iob == 2:
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return False
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elif preset_ent_iob == 3:
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return False
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elif st.B_(1).ent_iob == 2 or st.B_(1).ent_iob == 3:
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# If we know the next word is B or O, we can't be I (must be L)
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elif st.B_(1).ent_iob == 3:
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# If we know the next word is B, we can't be I (must be L)
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return False
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elif preset_ent_iob == 1:
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if st.B_(1).ent_iob in (0, 2):
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# if next preset is missing or O, this can't be I (must be L)
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return False
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elif label != preset_ent_label:
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# If label isn't right, reject
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return False
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else:
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# Otherwise, force acceptance, even if we're across a sentence
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# boundary or the token is whitespace.
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return True
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elif st.B(1) != -1 and st.B_(1).sent_start == 1:
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# Don't allow entities to extend across sentence boundaries
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return False
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@ -387,17 +397,24 @@ cdef class In:
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else:
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return 1
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cdef class Last:
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@staticmethod
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cdef bint is_valid(const StateC* st, attr_t label) nogil:
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cdef int preset_ent_iob = st.B_(0).ent_iob
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cdef attr_t preset_ent_label = st.B_(0).ent_type
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if label == 0:
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return False
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elif not st.entity_is_open():
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return False
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elif st.B_(0).ent_iob == 1 and st.B_(1).ent_iob != 1:
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elif preset_ent_iob == 1 and st.B_(1).ent_iob != 1:
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# If a preset entity has I followed by not-I, is L
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return True
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if label != preset_ent_label:
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# If label isn't right, reject
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return False
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else:
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# Otherwise, force acceptance, even if we're across a sentence
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# boundary or the token is whitespace.
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return True
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elif st.E_(0).ent_type != label:
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return False
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elif st.B_(1).ent_iob == 1:
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@ -450,12 +467,13 @@ cdef class Unit:
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cdef int preset_ent_iob = st.B_(0).ent_iob
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cdef attr_t preset_ent_label = st.B_(0).ent_type
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if label == 0:
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return False
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# this is only allowed if it's a preset blocked annotation
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if preset_ent_label == 0 and preset_ent_iob == 3:
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return True
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else:
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return False
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elif st.entity_is_open():
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return False
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elif preset_ent_iob == 2:
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# Don't clobber preset O
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return False
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elif st.B_(1).ent_iob == 1:
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# If next token is In, we can't be Unit -- must be Begin
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return False
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@ -135,7 +135,9 @@ cdef class Parser:
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names = []
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for i in range(self.moves.n_moves):
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name = self.moves.move_name(self.moves.c[i].move, self.moves.c[i].label)
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names.append(name)
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# Explicitly removing the internal "U-" token used for blocking entities
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if name != "U-":
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names.append(name)
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return names
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nr_feature = 8
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@ -16,10 +16,23 @@ def test_doc_add_entities_set_ents_iob(en_vocab):
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ner(doc)
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assert len(list(doc.ents)) == 0
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assert [w.ent_iob_ for w in doc] == (["O"] * len(doc))
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doc.ents = [(doc.vocab.strings["ANIMAL"], 3, 4)]
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assert [w.ent_iob_ for w in doc] == ["", "", "", "B"]
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assert [w.ent_iob_ for w in doc] == ["O", "O", "O", "B"]
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doc.ents = [(doc.vocab.strings["WORD"], 0, 2)]
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assert [w.ent_iob_ for w in doc] == ["B", "I", "", ""]
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assert [w.ent_iob_ for w in doc] == ["B", "I", "O", "O"]
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def test_ents_reset(en_vocab):
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text = ["This", "is", "a", "lion"]
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doc = get_doc(en_vocab, text)
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ner = EntityRecognizer(en_vocab)
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ner.begin_training([])
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ner(doc)
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assert [t.ent_iob_ for t in doc] == (["O"] * len(doc))
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doc.ents = list(doc.ents)
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assert [t.ent_iob_ for t in doc] == (["O"] * len(doc))
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def test_add_overlapping_entities(en_vocab):
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@ -2,7 +2,9 @@
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from __future__ import unicode_literals
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import pytest
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from spacy.pipeline import EntityRecognizer
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from spacy.lang.en import English
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from spacy.pipeline import EntityRecognizer, EntityRuler
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from spacy.vocab import Vocab
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from spacy.syntax.ner import BiluoPushDown
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from spacy.gold import GoldParse
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@ -80,14 +82,145 @@ def test_get_oracle_moves_negative_O(tsys, vocab):
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assert names
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def test_doc_add_entities_set_ents_iob(en_vocab):
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doc = Doc(en_vocab, words=["This", "is", "a", "lion"])
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ner = EntityRecognizer(en_vocab)
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ner.begin_training([])
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ner(doc)
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assert len(list(doc.ents)) == 0
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assert [w.ent_iob_ for w in doc] == (["O"] * len(doc))
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doc.ents = [(doc.vocab.strings["ANIMAL"], 3, 4)]
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assert [w.ent_iob_ for w in doc] == ["", "", "", "B"]
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doc.ents = [(doc.vocab.strings["WORD"], 0, 2)]
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assert [w.ent_iob_ for w in doc] == ["B", "I", "", ""]
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def test_accept_blocked_token():
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"""Test succesful blocking of tokens to be in an entity."""
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# 1. test normal behaviour
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nlp1 = English()
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doc1 = nlp1("I live in New York")
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ner1 = EntityRecognizer(doc1.vocab)
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assert [token.ent_iob_ for token in doc1] == ["", "", "", "", ""]
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assert [token.ent_type_ for token in doc1] == ["", "", "", "", ""]
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# Add the OUT action
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ner1.moves.add_action(5, "")
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ner1.add_label("GPE")
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# Get into the state just before "New"
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state1 = ner1.moves.init_batch([doc1])[0]
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ner1.moves.apply_transition(state1, "O")
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ner1.moves.apply_transition(state1, "O")
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ner1.moves.apply_transition(state1, "O")
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# Check that B-GPE is valid.
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assert ner1.moves.is_valid(state1, "B-GPE")
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# 2. test blocking behaviour
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nlp2 = English()
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doc2 = nlp2("I live in New York")
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ner2 = EntityRecognizer(doc2.vocab)
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# set "New York" to a blocked entity
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doc2.ents = [(0, 3, 5)]
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assert [token.ent_iob_ for token in doc2] == ["", "", "", "B", "B"]
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assert [token.ent_type_ for token in doc2] == ["", "", "", "", ""]
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# Check that B-GPE is now invalid.
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ner2.moves.add_action(4, "")
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ner2.moves.add_action(5, "")
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ner2.add_label("GPE")
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state2 = ner2.moves.init_batch([doc2])[0]
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ner2.moves.apply_transition(state2, "O")
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ner2.moves.apply_transition(state2, "O")
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ner2.moves.apply_transition(state2, "O")
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# we can only use U- for "New"
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assert not ner2.moves.is_valid(state2, "B-GPE")
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assert ner2.moves.is_valid(state2, "U-")
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ner2.moves.apply_transition(state2, "U-")
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# we can only use U- for "York"
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assert not ner2.moves.is_valid(state2, "B-GPE")
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assert ner2.moves.is_valid(state2, "U-")
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def test_overwrite_token():
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nlp = English()
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ner1 = nlp.create_pipe("ner")
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nlp.add_pipe(ner1, name="ner")
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nlp.begin_training()
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# The untrained NER will predict O for each token
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doc = nlp("I live in New York")
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assert [token.ent_iob_ for token in doc] == ["O", "O", "O", "O", "O"]
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assert [token.ent_type_ for token in doc] == ["", "", "", "", ""]
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# Check that a new ner can overwrite O
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ner2 = EntityRecognizer(doc.vocab)
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ner2.moves.add_action(5, "")
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ner2.add_label("GPE")
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state = ner2.moves.init_batch([doc])[0]
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assert ner2.moves.is_valid(state, "B-GPE")
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assert ner2.moves.is_valid(state, "U-GPE")
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ner2.moves.apply_transition(state, "B-GPE")
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assert ner2.moves.is_valid(state, "I-GPE")
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assert ner2.moves.is_valid(state, "L-GPE")
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def test_ruler_before_ner():
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""" Test that an NER works after an entity_ruler: the second can add annotations """
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nlp = English()
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# 1 : Entity Ruler - should set "this" to B and everything else to empty
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ruler = EntityRuler(nlp)
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patterns = [{"label": "THING", "pattern": "This"}]
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ruler.add_patterns(patterns)
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nlp.add_pipe(ruler)
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# 2: untrained NER - should set everything else to O
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untrained_ner = nlp.create_pipe("ner")
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untrained_ner.add_label("MY_LABEL")
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nlp.add_pipe(untrained_ner)
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nlp.begin_training()
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doc = nlp("This is Antti Korhonen speaking in Finland")
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expected_iobs = ["B", "O", "O", "O", "O", "O", "O"]
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expected_types = ["THING", "", "", "", "", "", ""]
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assert [token.ent_iob_ for token in doc] == expected_iobs
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assert [token.ent_type_ for token in doc] == expected_types
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def test_ner_before_ruler():
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""" Test that an entity_ruler works after an NER: the second can overwrite O annotations """
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nlp = English()
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# 1: untrained NER - should set everything to O
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untrained_ner = nlp.create_pipe("ner")
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untrained_ner.add_label("MY_LABEL")
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nlp.add_pipe(untrained_ner, name="uner")
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nlp.begin_training()
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# 2 : Entity Ruler - should set "this" to B and keep everything else O
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ruler = EntityRuler(nlp)
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patterns = [{"label": "THING", "pattern": "This"}]
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ruler.add_patterns(patterns)
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nlp.add_pipe(ruler)
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doc = nlp("This is Antti Korhonen speaking in Finland")
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expected_iobs = ["B", "O", "O", "O", "O", "O", "O"]
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expected_types = ["THING", "", "", "", "", "", ""]
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assert [token.ent_iob_ for token in doc] == expected_iobs
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assert [token.ent_type_ for token in doc] == expected_types
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def test_block_ner():
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""" Test functionality for blocking tokens so they can't be in a named entity """
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# block "Antti L Korhonen" from being a named entity
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nlp = English()
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nlp.add_pipe(BlockerComponent1(2, 5))
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untrained_ner = nlp.create_pipe("ner")
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untrained_ner.add_label("MY_LABEL")
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nlp.add_pipe(untrained_ner, name="uner")
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nlp.begin_training()
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doc = nlp("This is Antti L Korhonen speaking in Finland")
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expected_iobs = ["O", "O", "B", "B", "B", "O", "O", "O"]
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expected_types = ["", "", "", "", "", "", "", ""]
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assert [token.ent_iob_ for token in doc] == expected_iobs
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assert [token.ent_type_ for token in doc] == expected_types
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class BlockerComponent1(object):
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name = "my_blocker"
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def __init__(self, start, end):
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self.start = start
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self.end = end
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def __call__(self, doc):
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doc.ents = [(0, self.start, self.end)]
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return doc
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|
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@ -426,7 +426,7 @@ def test_issue957(en_tokenizer):
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def test_issue999(train_data):
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"""Test that adding entities and resuming training works passably OK.
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There are two issues here:
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1) We have to readd labels. This isn't very nice.
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1) We have to read labels. This isn't very nice.
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2) There's no way to set the learning rate for the weight update, so we
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end up out-of-scale, causing it to learn too fast.
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"""
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|
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42
spacy/tests/regression/test_issue4267.py
Normal file
42
spacy/tests/regression/test_issue4267.py
Normal file
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@ -0,0 +1,42 @@
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# coding: utf8
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from __future__ import unicode_literals
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import pytest
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import spacy
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from spacy.lang.en import English
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from spacy.pipeline import EntityRuler
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from spacy.tokens import Span
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def test_issue4267():
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""" Test that running an entity_ruler after ner gives consistent results"""
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nlp = English()
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ner = nlp.create_pipe("ner")
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ner.add_label("PEOPLE")
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nlp.add_pipe(ner)
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nlp.begin_training()
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assert "ner" in nlp.pipe_names
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# assert that we have correct IOB annotations
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doc1 = nlp("hi")
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assert doc1.is_nered
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for token in doc1:
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assert token.ent_iob == 2
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# add entity ruler and run again
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ruler = EntityRuler(nlp)
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patterns = [{"label": "SOFTWARE", "pattern": "spacy"}]
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ruler.add_patterns(patterns)
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nlp.add_pipe(ruler)
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assert "entity_ruler" in nlp.pipe_names
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assert "ner" in nlp.pipe_names
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# assert that we still have correct IOB annotations
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doc2 = nlp("hi")
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assert doc2.is_nered
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for token in doc2:
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assert token.ent_iob == 2
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@ -256,7 +256,7 @@ cdef class Doc:
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def is_nered(self):
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"""Check if the document has named entities set. Will return True if
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||||
*any* of the tokens has a named entity tag set (even if the others are
|
||||
uknown values).
|
||||
unknown values).
|
||||
"""
|
||||
if len(self) == 0:
|
||||
return True
|
||||
|
|
@ -525,13 +525,11 @@ cdef class Doc:
|
|||
|
||||
def __set__(self, ents):
|
||||
# TODO:
|
||||
# 1. Allow negative matches
|
||||
# 2. Ensure pre-set NERs are not over-written during statistical
|
||||
# prediction
|
||||
# 3. Test basic data-driven ORTH gazetteer
|
||||
# 4. Test more nuanced date and currency regex
|
||||
# 1. Test basic data-driven ORTH gazetteer
|
||||
# 2. Test more nuanced date and currency regex
|
||||
tokens_in_ents = {}
|
||||
cdef attr_t entity_type
|
||||
cdef attr_t kb_id
|
||||
cdef int ent_start, ent_end
|
||||
for ent_info in ents:
|
||||
entity_type, kb_id, ent_start, ent_end = get_entity_info(ent_info)
|
||||
|
|
@ -545,27 +543,31 @@ cdef class Doc:
|
|||
tokens_in_ents[token_index] = (ent_start, ent_end, entity_type, kb_id)
|
||||
cdef int i
|
||||
for i in range(self.length):
|
||||
self.c[i].ent_type = 0
|
||||
self.c[i].ent_kb_id = 0
|
||||
self.c[i].ent_iob = 0 # Means missing.
|
||||
cdef attr_t ent_type
|
||||
cdef int start, end
|
||||
for ent_info in ents:
|
||||
ent_type, ent_kb_id, start, end = get_entity_info(ent_info)
|
||||
if ent_type is None or ent_type < 0:
|
||||
# Mark as O
|
||||
for i in range(start, end):
|
||||
self.c[i].ent_type = 0
|
||||
self.c[i].ent_kb_id = 0
|
||||
self.c[i].ent_iob = 2
|
||||
else:
|
||||
# Mark (inside) as I
|
||||
for i in range(start, end):
|
||||
self.c[i].ent_type = ent_type
|
||||
self.c[i].ent_kb_id = ent_kb_id
|
||||
self.c[i].ent_iob = 1
|
||||
# Set start as B
|
||||
self.c[start].ent_iob = 3
|
||||
# default values
|
||||
entity_type = 0
|
||||
kb_id = 0
|
||||
|
||||
# Set ent_iob to Missing (0) bij default unless this token was nered before
|
||||
ent_iob = 0
|
||||
if self.c[i].ent_iob != 0:
|
||||
ent_iob = 2
|
||||
|
||||
# overwrite if the token was part of a specified entity
|
||||
if i in tokens_in_ents.keys():
|
||||
ent_start, ent_end, entity_type, kb_id = tokens_in_ents[i]
|
||||
if entity_type is None or entity_type <= 0:
|
||||
# Blocking this token from being overwritten by downstream NER
|
||||
ent_iob = 3
|
||||
elif ent_start == i:
|
||||
# Marking the start of an entity
|
||||
ent_iob = 3
|
||||
else:
|
||||
# Marking the inside of an entity
|
||||
ent_iob = 1
|
||||
|
||||
self.c[i].ent_type = entity_type
|
||||
self.c[i].ent_kb_id = kb_id
|
||||
self.c[i].ent_iob = ent_iob
|
||||
|
||||
@property
|
||||
def noun_chunks(self):
|
||||
|
|
|
|||
|
|
@ -749,7 +749,8 @@ cdef class Token:
|
|||
def ent_iob_(self):
|
||||
"""IOB code of named entity tag. "B" means the token begins an entity,
|
||||
"I" means it is inside an entity, "O" means it is outside an entity,
|
||||
and "" means no entity tag is set.
|
||||
and "" means no entity tag is set. "B" with an empty ent_type
|
||||
means that the token is blocked from further processing by NER.
|
||||
|
||||
RETURNS (unicode): IOB code of named entity tag.
|
||||
"""
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user