Replace PhraseMatcher with the Aho-Corasick algorithm over numpy arrays
of the hash values for the relevant attribute. The implementation is
based on FlashText.
The speed should be similar to the previous PhraseMatcher. It is now
possible to easily remove match IDs and matches don't go missing with
large keyword lists / vocabularies.
Fixes#4308.
* remove duplicate unit test
* unit test (currently failing) for issue 4267
* bugfix: ensure doc.ents preserves kb_id annotations
* fix in setting doc.ents with empty label
* rename
* test for presetting an entity to a certain type
* allow overwriting Outside + blocking presets
* fix actions when previous label needs to be kept
* fix default ent_iob in set entities
* cleaner solution with U- action
* remove debugging print statements
* unit tests with explicit transitions and is_valid testing
* remove U- from move_names explicitly
* remove unit tests with pre-trained models that don't work
* remove (working) unit tests with pre-trained models
* clean up unit tests
* move unit tests
* small fixes
* remove two TODO's from doc.ents comments
* make merge more efficient
* fix offsets
* merge works with relative indices
* remove printing
* Add the SCA
* fix SCA date
* more cythonize _retokenize.pyx
* more cythonize _retokenize.pyx
* fix only declaration in _retokenize.pyx
* switch back to absolute head
* switch back to absolute head
* fix comment
* merge from origin repo
* remove redundant __call__ method in pipes.TextCategorizer
Because the parent __call__ method behaves in the same way.
* fix: Pipe.__call__ arg
* fix: invalid arg in Pipe.__call__
* modified: spacy/tests/regression/test_issue4278.py (#4278)
* deleted: Pipfile
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.
Add Kannada, Tamil, and Telugu unicode blocks to uncased character
classes so that period is recognized as a suffix during tokenization.
(I'm sure a few symbols in the code blocks should not be ALPHA, but this
is mainly relevant for suffix detection and seems to be an improvement
in practice.)
* 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
