Bugfix/nel crossing sentence (#7630)

* ensure each entity gets a KB ID, even when it's not within a sentence

* cleanup

spacy/pipeline/entity_linker.py

@@ -300,10 +300,11 @@ class EntityLinker(TrainablePipe):
         for i, doc in enumerate(docs):
             sentences = [s for s in doc.sents]
             if len(doc) > 0:
-                # Looping through each sentence and each entity
-                # This may go wrong if there are entities across sentences - which shouldn't happen normally.
-                for sent_index, sent in enumerate(sentences):
-                    if sent.ents:
+                # Looping through each entity (TODO: rewrite)
+                for ent in doc.ents:
+                    sent = ent.sent
+                    sent_index = sentences.index(sent)
+                    assert sent_index >= 0
                     # get n_neightbour sentences, clipped to the length of the document
                     start_sentence = max(0, sent_index - self.n_sents)
                     end_sentence = min(
@@ -318,7 +319,6 @@ class EntityLinker(TrainablePipe):
                         sentence_encoding = self.model.predict([sent_doc])[0]
                         sentence_encoding_t = sentence_encoding.T
                         sentence_norm = xp.linalg.norm(sentence_encoding_t)
-                        for ent in sent.ents:
                     entity_count += 1
                     if ent.label_ in self.labels_discard:
                         # ignoring this entity - setting to NIL

spacy/tests/regression/test_issue7065.py

@@ -1,4 +1,6 @@
+from spacy.kb import KnowledgeBase
 from spacy.lang.en import English
+from spacy.training import Example
 
 
 def test_issue7065():
@@ -16,3 +18,58 @@ def test_issue7065():
     ent = doc.ents[0]
     assert ent.start < sent0.end < ent.end
     assert sentences.index(ent.sent) == 0
+
+
+def test_issue7065_b():
+    # Test that the NEL doesn't crash when an entity crosses a sentence boundary
+    nlp = English()
+    vector_length = 3
+    nlp.add_pipe("sentencizer")
+
+    text = "Mahler 's Symphony No. 8 was beautiful."
+    entities = [(0, 6, "PERSON"), (10, 24, "WORK")]
+    links = {(0, 6): {"Q7304": 1.0, "Q270853": 0.0},
+             (10, 24): {"Q7304": 0.0, "Q270853": 1.0}}
+    sent_starts = [1, -1, 0, 0, 0, 0, 0, 0, 0]
+    doc = nlp(text)
+    example = Example.from_dict(doc, {"entities": entities, "links": links, "sent_starts": sent_starts})
+    train_examples = [example]
+
+    def create_kb(vocab):
+        # create artificial KB
+        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb.add_entity(entity="Q270853", freq=12, entity_vector=[9, 1, -7])
+        mykb.add_alias(
+            alias="No. 8",
+            entities=["Q270853"],
+            probabilities=[1.0],
+        )
+        mykb.add_entity(entity="Q7304", freq=12, entity_vector=[6, -4, 3])
+        mykb.add_alias(
+            alias="Mahler",
+            entities=["Q7304"],
+            probabilities=[1.0],
+        )
+        return mykb
+
+    # Create the Entity Linker component and add it to the pipeline
+    entity_linker = nlp.add_pipe("entity_linker", last=True)
+    entity_linker.set_kb(create_kb)
+
+    # train the NEL pipe
+    optimizer = nlp.initialize(get_examples=lambda: train_examples)
+    for i in range(2):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+
+    # Add a custom rule-based component to mimick NER
+    patterns = [
+        {"label": "PERSON", "pattern": [{"LOWER": "mahler"}]},
+        {"label": "WORK", "pattern": [{"LOWER": "symphony"}, {"LOWER": "no"}, {"LOWER": "."}, {"LOWER": "8"}]}
+    ]
+    ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
+    ruler.add_patterns(patterns)
+
+    # test the trained model - this should not throw E148
+    doc = nlp(text)
+    assert doc