* Add Lemmatizer and simplify related components
* Add `Lemmatizer` pipe with `lookup` and `rule` modes using the
`Lookups` tables.
* Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma)
* Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer,
or morph rules)
* Remove lemmatizer from `Vocab`
* Adjust many many tests
Differences:
* No default lookup lemmas
* No special treatment of TAG in `from_array` and similar required
* Easier to modify labels in a `Tagger`
* No extra strings added from morphology / tag map
* Fix test
* Initial fix for Lemmatizer config/serialization
* Adjust init test to be more generic
* Adjust init test to force empty Lookups
* Add simple cache to rule-based lemmatizer
* Convert language-specific lemmatizers
Convert language-specific lemmatizers to component lemmatizers. Remove
previous lemmatizer class.
* Fix French and Polish lemmatizers
* Remove outdated UPOS conversions
* Update Russian lemmatizer init in tests
* Add minimal init/run tests for custom lemmatizers
* Add option to overwrite existing lemmas
* Update mode setting, lookup loading, and caching
* Make `mode` an immutable property
* Only enforce strict `load_lookups` for known supported modes
* Move caching into individual `_lemmatize` methods
* Implement strict when lang is not found in lookups
* Fix tables/lookups in make_lemmatizer
* Reallow provided lookups and allow for stricter checks
* Add lookups asset to all Lemmatizer pipe tests
* Rename lookups in lemmatizer init test
* Clean up merge
* Refactor lookup table loading
* Add helper from `load_lemmatizer_lookups` that loads required and
optional lookups tables based on settings provided by a config.
Additional slight refactor of lookups:
* Add `Lookups.set_table` to set a table from a provided `Table`
* Reorder class definitions to be able to specify type as `Table`
* Move registry assets into test methods
* Refactor lookups tables config
Use class methods within `Lemmatizer` to provide the config for
particular modes and to load the lookups from a config.
* Add pipe and score to lemmatizer
* Simplify Tagger.score
* Add missing import
* Clean up imports and auto-format
* Remove unused kwarg
* Tidy up and auto-format
* Update docstrings for Lemmatizer
Update docstrings for Lemmatizer.
Additionally modify `is_base_form` API to take `Token` instead of
individual features.
* Update docstrings
* Remove tag map values from Tagger.add_label
* Update API docs
* Fix relative link in Lemmatizer API docs
Update `Morphology` to load exceptions in `Morphology.__init__` and
`Morphology.load_morph_exceptions` from the format used in `MORPH_RULES`
rather than the internal format with tuple keys.
* Rename to `Morphology.exc` to `Morphology._exc` for internal use with
tuple keys
* Add `Morphology.exc` as a property that converts the internal `_exc`
back to `MORPH_RULES` format, primarily for serialization
* 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.
