mirror of https://github.com/explosion/spaCy.git
Merge branch 'master' into spacy.io
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commit
959bc616dd
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@ -36,7 +36,7 @@ for token in doc:
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| Text | Lemma | POS | Tag | Dep | Shape | alpha | stop |
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| ------- | ------- | ------- | ----- | ---------- | ------- | ------- | ------- |
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| Apple | apple | `PROPN` | `NNP` | `nsubj` | `Xxxxx` | `True` | `False` |
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| is | be | `VERB` | `VBZ` | `aux` | `xx` | `True` | `True` |
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| is | be | `AUX` | `VBZ` | `aux` | `xx` | `True` | `True` |
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| looking | look | `VERB` | `VBG` | `ROOT` | `xxxx` | `True` | `False` |
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| at | at | `ADP` | `IN` | `prep` | `xx` | `True` | `True` |
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| buying | buy | `VERB` | `VBG` | `pcomp` | `xxxx` | `True` | `False` |
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@ -662,7 +662,7 @@ One thing to keep in mind is that spaCy expects to train its models from **whole
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documents**, not just single sentences. If your corpus only contains single
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sentences, spaCy's models will never learn to expect multi-sentence documents,
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leading to low performance on real text. To mitigate this problem, you can use
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the `-N` argument to the `spacy convert` command, to merge some of the sentences
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the `-n` argument to the `spacy convert` command, to merge some of the sentences
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into longer pseudo-documents.
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### Training the tagger and parser {#train-tagger-parser}
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@ -471,7 +471,7 @@ doc = nlp.make_doc("London is a big city in the United Kingdom.")
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print("Before", doc.ents) # []
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header = [ENT_IOB, ENT_TYPE]
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attr_array = numpy.zeros((len(doc), len(header)))
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attr_array = numpy.zeros((len(doc), len(header)), dtype="uint64")
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attr_array[0, 0] = 3 # B
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attr_array[0, 1] = doc.vocab.strings["GPE"]
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doc.from_array(header, attr_array)
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@ -1143,9 +1143,9 @@ from spacy.gold import align
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other_tokens = ["i", "listened", "to", "obama", "'", "s", "podcasts", "."]
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spacy_tokens = ["i", "listened", "to", "obama", "'s", "podcasts", "."]
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cost, a2b, b2a, a2b_multi, b2a_multi = align(other_tokens, spacy_tokens)
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print("Misaligned tokens:", cost) # 2
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print("Edit distance:", cost) # 3
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print("One-to-one mappings a -> b", a2b) # array([0, 1, 2, 3, -1, -1, 5, 6])
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print("One-to-one mappings b -> a", b2a) # array([0, 1, 2, 3, 5, 6, 7])
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print("One-to-one mappings b -> a", b2a) # array([0, 1, 2, 3, -1, 6, 7])
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print("Many-to-one mappings a -> b", a2b_multi) # {4: 4, 5: 4}
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print("Many-to-one mappings b-> a", b2a_multi) # {}
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```
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@ -1153,7 +1153,7 @@ print("Many-to-one mappings b-> a", b2a_multi) # {}
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Here are some insights from the alignment information generated in the example
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above:
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- Two tokens are misaligned.
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- The edit distance (cost) is `3`: two deletions and one insertion.
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- The one-to-one mappings for the first four tokens are identical, which means
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they map to each other. This makes sense because they're also identical in the
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input: `"i"`, `"listened"`, `"to"` and `"obama"`.
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@ -1158,17 +1158,17 @@ what you need for your application.
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> available corpus.
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For example, the corpus spaCy's [English models](/models/en) were trained on
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defines a `PERSON` entity as just the **person name**, without titles like "Mr"
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or "Dr". This makes sense, because it makes it easier to resolve the entity type
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back to a knowledge base. But what if your application needs the full names,
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_including_ the titles?
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defines a `PERSON` entity as just the **person name**, without titles like "Mr."
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or "Dr.". This makes sense, because it makes it easier to resolve the entity
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type back to a knowledge base. But what if your application needs the full
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names, _including_ the titles?
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```python
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### {executable="true"}
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import spacy
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nlp = spacy.load("en_core_web_sm")
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doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
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doc = nlp("Dr. Alex Smith chaired first board meeting of Acme Corp Inc.")
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print([(ent.text, ent.label_) for ent in doc.ents])
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```
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@ -1233,7 +1233,7 @@ def expand_person_entities(doc):
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# Add the component after the named entity recognizer
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nlp.add_pipe(expand_person_entities, after='ner')
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doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
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doc = nlp("Dr. Alex Smith chaired first board meeting of Acme Corp Inc.")
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print([(ent.text, ent.label_) for ent in doc.ents])
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```
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