Instead of a hard-coded NER tag simplification function that was only
intended for NorNE, map NER tags in CoNLL-U converter using a dict
provided as JSON as a command-line option.
Map NER entity types or new tag or to "" for 'O', e.g.:
```
{"PER": "PERSON", "BAD": ""}
=>
B-PER -> B-PERSON
B-BAD -> O
```
* Update token.md
documentation is confusing: A '?' is a right punct, but '¿' is a left punct
* Update token.md
add quotations around parentheses in `is_left_punct` and `is_right_punct` for clarrification, ensuring the question mark that follows is not percieved as an example of left and right punctuation
* Move quotes into code block [ci skip]
* Include Doc.cats in to_bytes()
* Include Doc.cats in DocBin serialization
* Add tests for serialization of cats
Test serialization of cats for Doc and DocBin.
* Enable lex_attrs on Finnish
* Copy the Danish tokenizer rules to Finnish
Specifically, don't break hyphenated compound words
* Contributor agreement
* A new file for Finnish tokenizer rules instead of including the Danish ones
- added some tests for tokenization issues
- fixed some issues with tokenization of words with hyphen infix
- rewrote the "tokenizer_exceptions.py" file (stemming from the German version)
* Add sent_starts to GoldParse
* Add SentTagger pipeline component
Add `SentTagger` pipeline component as a subclass of `Tagger`.
* Model reduces default parameters from `Tagger` to be small and fast
* Hard-coded set of two labels:
* S (1): token at beginning of sentence
* I (0): all other sentence positions
* Sets `token.sent_start` values
* Add sentence segmentation to Scorer
Report `sent_p/r/f` for sentence boundaries, which may be provided by
various pipeline components.
* Add sentence segmentation to CLI evaluate
* Add senttagger metrics/scoring to train CLI
* Rename SentTagger to SentenceRecognizer
* Add SentenceRecognizer to spacy.pipes imports
* Add SentenceRecognizer serialization test
* Shorten component name to sentrec
* Remove duplicates from train CLI output metrics
* Restructure Sentencizer to follow Pipe API
Restructure Sentencizer to follow Pipe API so that it can be scored with
`nlp.evaluate()`.
* Add Sentencizer pipe() test
Replace old gold alignment that allowed for some noise in the alignment between raw and orth with the new simpler alignment that requires that the raw and orth strings are identical except for whitespace and capitalization.
* Replace old alignment with new alignment, removing `_align.pyx` and
its tests
* Remove all quote normalizations
* Enable test for new align
* Modify test case for quote normalization
* Switch to train_dataset() function in train CLI
* Fixes for pipe() methods in pipeline components
* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`
* Update Parser.pipe() for Examples
* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s
* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)
* Fixes to Example implementation in spacy.gold
* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`
* Head of 0 are not treated as unset
* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)
* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example
* Don't clobber `examples` variable in `iter_gold_docs()`
* Add/modify gold tests for handling projectivity
* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)
* Add test for projective train vs. nonprojective dev versions of the
same `Doc`
* Handle ignore_misaligned as arg rather than attr
Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.
Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).
* Remove unused attrs from gold.pxd
Remove `ignore_misaligned` and `make_projective` from `gold.pxd`
* Restructure Example with merged sents as default
An `Example` now includes a single `TokenAnnotation` that includes all
the information from one `Doc` (=JSON `paragraph`). If required, the
individual sentences can be returned as a list of examples with
`Example.split_sents()` with no raw text available.
* Input/output a single `Example.token_annotation`
* Add `sent_starts` to `TokenAnnotation` to handle sentence boundaries
* Replace `Example.merge_sents()` with `Example.split_sents()`
* Modify components to use a single `Example.token_annotation`
* Pipeline components
* conllu2json converter
* Rework/rename `add_token_annotation()` and `add_doc_annotation()` to
`set_token_annotation()` and `set_doc_annotation()`, functions that set
rather then appending/extending.
* Rename `morphology` to `morphs` in `TokenAnnotation` and `GoldParse`
* Add getters to `TokenAnnotation` to supply default values when a given
attribute is not available
* `Example.get_gold_parses()` in `spacy.gold._make_golds()` is only
applied on single examples, so the `GoldParse` is returned saved in the
provided `Example` rather than creating a new `Example` with no other
internal annotation
* Update tests for API changes and `merge_sents()` vs. `split_sents()`
* Refer to Example.goldparse in iter_gold_docs()
Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError
* Fix make_orth_variants()
Fix bug in make_orth_variants() related to conversion from multiple to
one TokenAnnotation per Example.
* Add basic test for make_orth_variants()
* Replace try/except with conditionals
* Replace default morph value with set
Iterate over lr_edges until all heads are within the current sentence.
Instead of iterating over them for a fixed number of iterations, check
whether the sentence boundaries are correct for the heads and stop when
all are correct. Stop after a maximum of 10 iterations, providing a
warning in this case since the sentence boundaries may not be correct.
* Switch to train_dataset() function in train CLI
* Fixes for pipe() methods in pipeline components
* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`
* Update Parser.pipe() for Examples
* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s
* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)
* Fixes to Example implementation in spacy.gold
* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`
* Head of 0 are not treated as unset
* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)
* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example
* Don't clobber `examples` variable in `iter_gold_docs()`
* Add/modify gold tests for handling projectivity
* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)
* Add test for projective train vs. nonprojective dev versions of the
same `Doc`
* Handle ignore_misaligned as arg rather than attr
Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.
Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).
* Remove unused attrs from gold.pxd
Remove `ignore_misaligned` and `make_projective` from `gold.pxd`
* Refer to Example.goldparse in iter_gold_docs()
Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError
* Update test for ignore_misaligned
* Switch from mecab-python3 to fugashi
mecab-python3 has been the best MeCab binding for a long time but it's
not very actively maintained, and since it's based on old SWIG code
distributed with MeCab there's a limit to how effectively it can be
maintained.
Fugashi is a new Cython-based MeCab wrapper I wrote. Since it's not
based on the old SWIG code it's easier to keep it current and make small
deviations from the MeCab C/C++ API where that makes sense.
* Change mecab-python3 to fugashi in setup.cfg
* Change "mecab tags" to "unidic tags"
The tags come from MeCab, but the tag schema is specified by Unidic, so
it's more proper to refer to it that way.
* Update conftest
* Add fugashi link to external deps list for Japanese
* Detect more empty matches in tokenizer.explain()
* Include a few languages in explain non-slow tests
Mark a few languages in tokenizer.explain() tests as not slow so they're
run by default.
