4.5 KiB
| title | teaser | menu | ||||||
|---|---|---|---|---|---|---|---|---|
| What's New in v3.1 | New features and how to upgrade |
|
New Features
Using predicted annotations during training
This project shows how to use the token.dep attribute predicted by the parser
as a feature for a subsequent tagger component in the pipeline.
SpanCategorizer for predicting arbitrary and overlapping spans
A common task in applied NLP is extracting spans of texts from documents,
including longer phrases or nested expressions. Named entity recognition isn't
the right tool for this problem, since an entity recognizer typically predicts
single token-based tags that are very sensitive to boundaries. This is effective
for proper nouns and self-contained expressions, but less useful for other types
of phrases or overlapping spans. The new
SpanCategorizer component and
SpanCategorizer architecture let you label
arbitrary and potentially overlapping spans of texts. A span categorizer
consists of two parts: a suggester function
that proposes candidate spans, which may or may not overlap, and a labeler model
that predicts zero or more labels for each candidate. The predicted spans are
available via the Doc.spans container.
The upcoming version of our annotation tool Prodigy
(currently available as a pre-release for all
users) features a new workflow and UI for
annotating overlapping and nested spans. You can use it to create training data
for spaCy's SpanCategorizer component.
Update the entity recognizer with partial incorrect annotations
config.cfg (excerpt)
[components.ner] factory = "ner" incorrect_spans_key = "incorrect_spans" moves = null update_with_oracle_cut_size = 100
The EntityRecognizer can now be updated with known
incorrect annotations, which lets you take advantage of partial and sparse data.
For example, you'll be able to use the information that certain spans of text
are definitely not PERSON entities, without having to provide the
complete-gold standard annotations for the given example. The incorrect span
annotations can be added via the Doc.spans in the training
data under the key defined as
incorrect_spans_key in the component config.
New pipeline packages for Catalan and Danish
| Package | Language | Tagger | Parser | NER |
|---|---|---|---|---|
ca_core_news_sm |
Catalan | |||
ca_core_news_md |
Catalan | |||
ca_core_news_lg |
Catalan | |||
ca_core_news_trf |
Catalan | |||
da_core_news_trf |
Danish |
Resizable text classification architectures
CLI command to assemble pipeline from config
The spacy assemble command lets you assemble a pipeline
from a config file without additional training. It can be especially useful for
creating a blank pipeline with a custom tokenizer, rule-based components or word
vectors.
$ python -m spacy assemble config.cfg ./output