Most of these characters are for languages / writing systems that aren't
supported by spacy, but I don't think it causes problems to include
them. In the UD evals, Hindi and Urdu improve a lot as expected (from
0-10% to 70-80%) and Persian improves a little (90% to 96%). Tamil
improves in combination with #4288.
The punctuation list is converted to a set internally because of its
increased length.
Sentence final punctuation generated with:
```
unichars -gas '[\p{Sentence_Break=STerm}\p{Sentence_Break=ATerm}]' '\p{Terminal_Punctuation}'
```
See: https://stackoverflow.com/a/9508766/461847Fixes#4269.
* Allow copying the user_data with as_doc + unit test
* add option to docs
* add typing
* import fix
* workaround to avoid bool clashing ...
* bint instead of bool
* Improve load_language_data helper
* WIP: Add Lookups implementation
* Start moving lemma data over to JSON
* WIP: move data over for more languages
* Convert more languages
* Fix lemmatizer fixtures in tests
* Finish conversion
* Auto-format JSON files
* Fix test for now
* Make sure tables are stored on instance
* Update docstrings
* Update docstrings and errors
* Update test
* Add Lookups.__len__
* Add serialization methods
* Add Lookups.remove_table
* Use msgpack for serialization to disk
* Fix file exists check
* Try using OrderedDict for everything
* Update .flake8 [ci skip]
* Try fixing serialization
* Update test_lookups.py
* Update test_serialize_vocab_strings.py
* Fix serialization for lookups
* Fix lookups
* Fix lookups
* Fix lookups
* Try to fix serialization
* Try to fix serialization
* Try to fix serialization
* Try to fix serialization
* Give up on serialization test
* Xfail more serialization tests for 3.5
* Fix lookups for 2.7
* Modify retokenizer to use span root attributes
* tag/pos/morph are set to root tag/pos/morph
* lemma and norm are reset and end up as orth (not ideal, but better
than orth of first token)
* Also handle individual merge case
* Add test
* Attempt to handle ent_iob and ent_type in merges
* Fix check for whether B-ENT should become I-ENT
* Move IOB consistency check to after attrs
Move all IOB consistency checks after attrs are set and simplify to
check entire document, modifying I to B at the beginning of the document
or if the entity type of the previous token isn't the same.
* Move IOB consistency check for single merge
Move IOB consistency check after the token array is compressed for the
single merge case.
* Update spacy/tokens/_retokenize.pyx
Co-Authored-By: Matthew Honnibal <honnibal+gh@gmail.com>
* Remove single vs. multiple merge distinction
Remove original single-instance `_merge()` and use `_bulk_merge()` (now
renamed `_merge()`) for all merges.
* Add out-of-bound check in previous entity check
* Updates/bugfixes for NER/IOB converters
* Converter formats `ner` and `iob` use autodetect to choose a converter if
possible
* `iob2json` is reverted to handle sentence-per-line data like
`word1|pos1|ent1 word2|pos2|ent2`
* Fix bug in `merge_sentences()` so the second sentence in each batch isn't
skipped
* `conll_ner2json` is made more general so it can handle more formats with
whitespace-separated columns
* Supports all formats where the first column is the token and the final
column is the IOB tag; if present, the second column is the POS tag
* As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O`
separates documents
* Add option for segmenting sentences (new flag `-s`)
* Parser-based sentence segmentation with a provided model, otherwise with
sentencizer (new option `-b` to specify model)
* Can group sentences into documents with `n_sents` as long as sentence
segmentation is available
* Only applies automatic segmentation when there are no existing delimiters
in the data
* Provide info about settings applied during conversion with warnings and
suggestions if settings conflict or might not be not optimal.
* Add tests for common formats
* Add '(default)' back to docs for -c auto
* Add document count back to output
* Revert changes to converter output message
* Use explicit tabs in convert CLI test data
* Adjust/add messages for n_sents=1 default
* Add sample NER data to training examples
* Update README
* Add links in docs to example NER data
* Define msg within converters
* Prevent subtok label if not learning tokens
The parser introduces the subtok label to mark tokens that should be
merged during post-processing. Previously this happened even if we did
not have the --learn-tokens flag set. This patch passes the config
through to the parser, to prevent the problem.
* Make merge_subtokens a parser post-process if learn_subtokens
* Fix train script
* Add test for 3830: subtok problem
* Fix handlign of non-subtok in parser training
* allow phrasematcher to link one match to multiple original patterns
* small fix for defining ent_id in the matcher (anti-ghost prevention)
* cleanup
* formatting
* Improve load_language_data helper
* WIP: Add Lookups implementation
* Start moving lemma data over to JSON
* WIP: move data over for more languages
* Convert more languages
* Fix lemmatizer fixtures in tests
* Finish conversion
* Auto-format JSON files
* Fix test for now
* Make sure tables are stored on instance
Check for relevant components in the pipeline when Matcher is called,
similar to the checks for PhraseMatcher in #4105.
* keep track of attributes seen in patterns
* when Matcher is called on a Doc, check for is_tagged for LEMMA, TAG,
POS and for is_parsed for DEP
* Fix typo in rule-based matching docs
* Improve token pattern checking without validation
Add more detailed token pattern checks without full JSON pattern validation and
provide more detailed error messages.
Addresses #4070 (also related: #4063, #4100).
* Check whether top-level attributes in patterns and attr for PhraseMatcher are
in token pattern schema
* Check whether attribute value types are supported in general (as opposed to
per attribute with full validation)
* Report various internal error types (OverflowError, AttributeError, KeyError)
as ValueError with standard error messages
* Check for tagger/parser in PhraseMatcher pipeline for attributes TAG, POS,
LEMMA, and DEP
* Add error messages with relevant details on how to use validate=True or nlp()
instead of nlp.make_doc()
* Support attr=TEXT for PhraseMatcher
* Add NORM to schema
* Expand tests for pattern validation, Matcher, PhraseMatcher, and EntityRuler
* Remove unnecessary .keys()
* Rephrase error messages
* Add another type check to Matcher
Add another type check to Matcher for more understandable error messages
in some rare cases.
* Support phrase_matcher_attr=TEXT for EntityRuler
* Don't use spacy.errors in examples and bin scripts
* Fix error code
* Auto-format
Also try get Azure pipelines to finally start a build :(
* Update errors.py
Co-authored-by: Ines Montani <ines@ines.io>
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Check whether two entities overlap
- biluo_gold_biluo_overlap now throw exception when entities passed in have overlaps
- added unit test
* SCA agreement
* pytest file for issue4104 established
* edited default lookup english lemmatizer for spun; fixes issue 4102
* eliminated parameterization and sorted dictionary dependnency in issue 4104 test
* added contributor agreement
* document token ent_kb_id
* document span kb_id
* update pipeline documentation
* prior and context weights as bool's instead
* entitylinker api documentation
* drop for both models
* finish entitylinker documentation
* small fixes
* documentation for KB
* candidate documentation
* links to api pages in code
* small fix
* frequency examples as counts for consistency
* consistent documentation about tensors returned by predict
* add entity linking to usage 101
* add entity linking infobox and KB section to 101
* entity-linking in linguistic features
* small typo corrections
* training example and docs for entity_linker
* predefined nlp and kb
* revert back to similarity encodings for simplicity (for now)
* set prior probabilities to 0 when excluded
* code clean up
* bugfix: deleting kb ID from tokens when entities were removed
* refactor train el example to use either model or vocab
* pretrain_kb example for example kb generation
* add to training docs for KB + EL example scripts
* small fixes
* error numbering
* ensure the language of vocab and nlp stay consistent across serialization
* equality with =
* avoid conflict in errors file
* add error 151
* final adjustements to the train scripts - consistency
* update of goldparse documentation
* small corrections
* push commit
* turn kb_creator into CLI script (wip)
* proper parameters for training entity vectors
* wikidata pipeline split up into two executable scripts
* remove context_width
* move wikidata scripts in bin directory, remove old dummy script
* refine KB script with logs and preprocessing options
* small edits
* small improvements to logging of EL CLI script
* Improve NER per type scoring
* include all gold labels in per type scoring, not only when recall > 0
* improve efficiency of per type scoring
* Create Scorer tests, initially with NER tests
* move regression test #3968 (per type NER scoring) to Scorer tests
* add new test for per type NER scoring with imperfect P/R/F and per
type P/R/F including a case where R == 0.0
