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Ines Montani 2020-08-25 13:27:59 +02:00
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website/docs/usage/linguistic-features.md
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@ -82,6 +82,14 @@ check whether a [`Doc`](/api/doc) object has been parsed with the
`doc.is_parsed` attribute, which returns a boolean value. If this attribute is
`False`, the default sentence iterator will raise an exception.
<Infobox title="Dependency label scheme" emoji="📖">
For a list of the syntactic dependency labels assigned by spaCy's models across
different languages, see the label schemes documented in the
[models directory](/models).
</Infobox>
### Noun chunks {#noun-chunks}
Noun chunks are "base noun phrases" – flat phrases that have a noun as their
@ -288,11 +296,45 @@ for token in doc:
| their | `ADJ` | `poss` | requests |
| requests | `NOUN` | `dobj` | submit |
<Infobox title="Dependency label scheme" emoji="📖">
The dependency parse can be a useful tool for **information extraction**,
especially when combined with other predictions like
[named entities](#named-entities). The following example extracts money and
currency values, i.e. entities labeled as `MONEY`, and then uses the dependency
parse to find the noun phrase they are referring to – for example `"Net income"`
&rarr; `"$9.4 million"`.
For a list of the syntactic dependency labels assigned by spaCy's models across
different languages, see the label schemes documented in the
[models directory](/models).
```python
### {executable="true"}
import spacy
nlp = spacy.load("en_core_web_sm")
# Merge noun phrases and entities for easier analysis
nlp.add_pipe("merge_entities")
nlp.add_pipe("merge_noun_chunks")
TEXTS = [
"Net income was $9.4 million compared to the prior year of $2.7 million.",
"Revenue exceeded twelve billion dollars, with a loss of $1b.",
]
for doc in nlp.pipe(TEXTS):
for token in doc:
if token.ent_type_ == "MONEY":
# We have an attribute and direct object, so check for subject
if token.dep_ in ("attr", "dobj"):
subj = [w for w in token.head.lefts if w.dep_ == "nsubj"]
if subj:
print(subj[0], "-->", token)
# We have a prepositional object with a preposition
elif token.dep_ == "pobj" and token.head.dep_ == "prep":
print(token.head.head, "-->", token)
```
<Infobox title="Combining models and rules" emoji="📖">
For more examples of how to write rule-based information extraction logic that
takes advantage of the model's predictions produced by the different components,
see the usage guide on
[combining models and rules](/usage/rule-based-matching#models-rules).
</Infobox>
@ -545,7 +587,7 @@ identifier from a knowledge base (KB). You can create your own
[train a new Entity Linking model](/usage/training#entity-linker) using that
custom-made KB.
### Accessing entity identifiers {#entity-linking-accessing}
### Accessing entity identifiers {#entity-linking-accessing model="entity linking"}
The annotated KB identifier is accessible as either a hash value or as a string,
using the attributes `ent.kb_id` and `ent.kb_id_` of a [`Span`](/api/span)
@ -571,15 +613,6 @@ print(ent_ada_1) # ['Lovelace', 'PERSON', 'Q7259']
print(ent_london_5) # ['London', 'GPE', 'Q84']
```
| Text | ent_type\_ | ent_kb_id\_ |
| -------- | ---------- | ----------- |
| Ada | `"PERSON"` | `"Q7259"` |
| Lovelace | `"PERSON"` | `"Q7259"` |
| was | - | - |
| born | - | - |
| in | - | - |
| London | `"GPE"` | `"Q84"` |
## Tokenization {#tokenization}
Tokenization is the task of splitting a text into meaningful segments, called