mirror of https://github.com/explosion/spaCy.git
adc9745718
* Restructure tag maps for MorphAnalysis changes
Prepare tag maps for upcoming MorphAnalysis changes that allow
arbritrary features.
* Use default tag map rather than duplicating for ca / uk / vi
* Import tag map into defaults for ga
* Modify tag maps so all morphological fields and features are strings
* Move features from `"Other"` to the top level
* Rewrite tuples as strings separated by `","`
* Rewrite morph symbols for fr lemmatizer as strings
* Export MorphAnalysis under spacy.tokens
* Modify morphology to support arbitrary features
Modify `Morphology` and `MorphAnalysis` so that arbitrary features are
supported.
* Modify `MorphAnalysisC` so that it can support arbitrary features and
multiple values per field. `MorphAnalysisC` is redesigned to contain:
* key: hash of UD FEATS string of morphological features
* array of `MorphFeatureC` structs that each contain a hash of `Field`
and `Field=Value` for a given morphological feature, which makes it
possible to:
* find features by field
* represent multiple values for a given field
* `get_field()` is renamed to `get_by_field()` and is no longer `nogil`.
Instead a new helper function `get_n_by_field()` is `nogil` and returns
`n` features by field.
* `MorphAnalysis.get()` returns all possible values for a field as a
list of individual features such as `["Tense=Pres", "Tense=Past"]`.
* `MorphAnalysis`'s `str()` and `repr()` are the UD FEATS string.
* `Morphology.feats_to_dict()` converts a UD FEATS string to a dict
where:
* Each field has one entry in the dict
* Multiple values remain separated by a separator in the value string
* `Token.morph_` returns the UD FEATS string and you can set
`Token.morph_` with a UD FEATS string or with a tag map dict.
* Modify get_by_field to use np.ndarray
Modify `get_by_field()` to use np.ndarray. Remove `max_results` from
`get_n_by_field()` and always iterate over all the fields.
* Rewrite without MorphFeatureC
* Add shortcut for existing feats strings as keys
Add shortcut for existing feats strings as keys in `Morphology.add()`.
* Check for '_' as empty analysis when adding morphs
* Extend helper converters in Morphology
Add and extend helper converters that convert and normalize between:
* UD FEATS strings (`"Case=dat,gen|Number=sing"`)
* per-field dict of feats (`{"Case": "dat,gen", "Number": "sing"}`)
* list of individual features (`["Case=dat", "Case=gen",
"Number=sing"]`)
All converters sort fields and values where applicable.
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| .. | ||
| cli | ||
| data | ||
| displacy | ||
| lang | ||
| matcher | ||
| ml | ||
| pipeline | ||
| syntax | ||
| tests | ||
| tokens | ||
| __init__.pxd | ||
| __init__.py | ||
| __main__.py | ||
| _ml.py | ||
| about.py | ||
| analysis.py | ||
| attrs.pxd | ||
| attrs.pyx | ||
| compat.py | ||
| errors.py | ||
| glossary.py | ||
| gold.pxd | ||
| gold.pyx | ||
| kb.pxd | ||
| kb.pyx | ||
| language.py | ||
| lemmatizer.py | ||
| lexeme.pxd | ||
| lexeme.pyx | ||
| lookups.py | ||
| morphology.pxd | ||
| morphology.pyx | ||
| parts_of_speech.pxd | ||
| parts_of_speech.pyx | ||
| schemas.py | ||
| scorer.py | ||
| strings.pxd | ||
| strings.pyx | ||
| structs.pxd | ||
| symbols.pxd | ||
| symbols.pyx | ||
| tokenizer.pxd | ||
| tokenizer.pyx | ||
| typedefs.pxd | ||
| typedefs.pyx | ||
| util.py | ||
| vectors.pyx | ||
| vocab.pxd | ||
| vocab.pyx | ||