%global _empty_manifest_terminate_build 0
Name: python-negspacy
Version: 1.0.3
Release: 1
Summary: A spaCy pipeline object for negation.
License: MIT
URL: https://github.com/jenojp/negspacy
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ed/e2/6adadc26af328268522da923aef42dfb3ae88e49122e54c34062f13505b3/negspacy-1.0.3.tar.gz
BuildArch: noarch
%description
# negspacy: negation for spaCy
[](https://dev.azure.com/jenopizzaro/negspacy/_build/latest?definitionId=2&branchName=master) [](https://spacy.io) [](https://pypi.org/project/negspacy/) [](https://zenodo.org/badge/latestdoi/201071164) [](https://github.com/ambv/black)
spaCy pipeline object for negating concepts in text. Based on the NegEx algorithm.
***NegEx - A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries
Chapman, Bridewell, Hanbury, Cooper, Buchanan***
[https://doi.org/10.1006/jbin.2001.1029](https://doi.org/10.1006/jbin.2001.1029)
## What's new
Version 1.0 is a major version update providing support for spaCy 3.0's new interface for adding pipeline components. As a result, it is not backwards compatible with previous versions of negspacy.
If your project uses spaCy 2.3.5 or earlier, you will need to use version 0.1.9. See [archived readme](https://github.com/jenojp/negspacy/blob/v0.1.9_spacy_2.3.5/README.md).
## Installation and usage
Install the library.
```bash
pip install negspacy
```
Import library and spaCy.
```python
import spacy
from negspacy.negation import Negex
```
Load spacy language model. Add negspacy pipeline object. Filtering on entity types is optional.
```python
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("negex", config={"ent_types":["PERSON","ORG"]})
```
View negations.
```python
doc = nlp("She does not like Steve Jobs but likes Apple products.")
for e in doc.ents:
print(e.text, e._.negex)
```
```console
Steve Jobs True
Apple False
```
Consider pairing with [scispacy](https://allenai.github.io/scispacy/) to find UMLS concepts in text and process negations.
## NegEx Patterns
* **pseudo_negations** - phrases that are false triggers, ambiguous negations, or double negatives
* **preceding_negations** - negation phrases that precede an entity
* **following_negations** - negation phrases that follow an entity
* **termination** - phrases that cut a sentence in parts, for purposes of negation detection (.e.g., "but")
### Termsets
Designate termset to use, `en_clinical` is used by default.
* `en` = phrases for general english language text
* `en_clinical` **DEFAULT** = adds phrases specific to clinical domain to general english
* `en_clinical_sensitive` = adds additional phrases to help rule out historical and possibly irrelevant entities
To set:
```python
from negspacy.negation import Negex
from negspacy.termsets import termset
ts = termset("en")
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(
"negex",
config={
"neg_termset":ts.get_patterns()
}
)
```
## Additional Functionality
### Change patterns or view patterns in use
Replace all patterns with your own set
```python
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(
"negex",
config={
"neg_termset":{
"pseudo_negations": ["might not"],
"preceding_negations": ["not"],
"following_negations":["declined"],
"termination": ["but","however"]
}
}
)
```
Add and remove individual patterns on the fly from built-in termsets
```python
from negspacy.termsets import termset
ts = termset("en")
ts.add_patterns({
"pseudo_negations": ["my favorite pattern"],
"termination": ["these are", "great patterns", "but"],
"preceding_negations": ["wow a negation"],
"following_negations": ["extra negation"],
})
#OR
ts.remove_patterns(
{
"termination": ["these are", "great patterns"],
"pseudo_negations": ["my favorite pattern"],
"preceding_negations": ["denied", "wow a negation"],
"following_negations": ["unlikely", "extra negation"],
}
)
```
View patterns in use
```python
from negspacy.termsets import termset
ts = termset("en_clinical")
print(ts.get_patterns())
```
### Negations in noun chunks
Depending on the Named Entity Recognition model you are using, you _may_ have negations "chunked together" with nouns. For example:
```python
nlp = spacy.load("en_core_sci_sm")
doc = nlp("There is no headache.")
for e in doc.ents:
print(e.text)
# no headache
```
This would cause the Negex algorithm to miss the preceding negation. To account for this, you can add a ```chunk_prefix```:
```python
nlp = spacy.load("en_core_sci_sm")
ts = termset("en_clinical")
nlp.add_pipe(
"negex",
config={
"chunk_prefix": ["no"],
},
last=True,
)
doc = nlp("There is no headache.")
for e in doc.ents:
print(e.text, e._.negex)
# no headache True
```
## Contributing
[contributing](https://github.com/jenojp/negspacy/blob/master/CONTRIBUTING.md)
## Authors
* Jeno Pizarro
## License
[license](https://github.com/jenojp/negspacy/blob/master/LICENSE)
## Other libraries
This library is featured in the [spaCy Universe](https://spacy.io/universe). Check it out for other useful libraries and inspiration.
If you're looking for a spaCy pipeline object to extract values that correspond to a named entity (e.g., birth dates, account numbers, or laboratory results) take a look at [extractacy](https://github.com/jenojp/extractacy).
%package -n python3-negspacy
Summary: A spaCy pipeline object for negation.
Provides: python-negspacy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-negspacy
# negspacy: negation for spaCy
[](https://dev.azure.com/jenopizzaro/negspacy/_build/latest?definitionId=2&branchName=master) [](https://spacy.io) [](https://pypi.org/project/negspacy/) [](https://zenodo.org/badge/latestdoi/201071164) [](https://github.com/ambv/black)
spaCy pipeline object for negating concepts in text. Based on the NegEx algorithm.
***NegEx - A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries
Chapman, Bridewell, Hanbury, Cooper, Buchanan***
[https://doi.org/10.1006/jbin.2001.1029](https://doi.org/10.1006/jbin.2001.1029)
## What's new
Version 1.0 is a major version update providing support for spaCy 3.0's new interface for adding pipeline components. As a result, it is not backwards compatible with previous versions of negspacy.
If your project uses spaCy 2.3.5 or earlier, you will need to use version 0.1.9. See [archived readme](https://github.com/jenojp/negspacy/blob/v0.1.9_spacy_2.3.5/README.md).
## Installation and usage
Install the library.
```bash
pip install negspacy
```
Import library and spaCy.
```python
import spacy
from negspacy.negation import Negex
```
Load spacy language model. Add negspacy pipeline object. Filtering on entity types is optional.
```python
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("negex", config={"ent_types":["PERSON","ORG"]})
```
View negations.
```python
doc = nlp("She does not like Steve Jobs but likes Apple products.")
for e in doc.ents:
print(e.text, e._.negex)
```
```console
Steve Jobs True
Apple False
```
Consider pairing with [scispacy](https://allenai.github.io/scispacy/) to find UMLS concepts in text and process negations.
## NegEx Patterns
* **pseudo_negations** - phrases that are false triggers, ambiguous negations, or double negatives
* **preceding_negations** - negation phrases that precede an entity
* **following_negations** - negation phrases that follow an entity
* **termination** - phrases that cut a sentence in parts, for purposes of negation detection (.e.g., "but")
### Termsets
Designate termset to use, `en_clinical` is used by default.
* `en` = phrases for general english language text
* `en_clinical` **DEFAULT** = adds phrases specific to clinical domain to general english
* `en_clinical_sensitive` = adds additional phrases to help rule out historical and possibly irrelevant entities
To set:
```python
from negspacy.negation import Negex
from negspacy.termsets import termset
ts = termset("en")
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(
"negex",
config={
"neg_termset":ts.get_patterns()
}
)
```
## Additional Functionality
### Change patterns or view patterns in use
Replace all patterns with your own set
```python
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(
"negex",
config={
"neg_termset":{
"pseudo_negations": ["might not"],
"preceding_negations": ["not"],
"following_negations":["declined"],
"termination": ["but","however"]
}
}
)
```
Add and remove individual patterns on the fly from built-in termsets
```python
from negspacy.termsets import termset
ts = termset("en")
ts.add_patterns({
"pseudo_negations": ["my favorite pattern"],
"termination": ["these are", "great patterns", "but"],
"preceding_negations": ["wow a negation"],
"following_negations": ["extra negation"],
})
#OR
ts.remove_patterns(
{
"termination": ["these are", "great patterns"],
"pseudo_negations": ["my favorite pattern"],
"preceding_negations": ["denied", "wow a negation"],
"following_negations": ["unlikely", "extra negation"],
}
)
```
View patterns in use
```python
from negspacy.termsets import termset
ts = termset("en_clinical")
print(ts.get_patterns())
```
### Negations in noun chunks
Depending on the Named Entity Recognition model you are using, you _may_ have negations "chunked together" with nouns. For example:
```python
nlp = spacy.load("en_core_sci_sm")
doc = nlp("There is no headache.")
for e in doc.ents:
print(e.text)
# no headache
```
This would cause the Negex algorithm to miss the preceding negation. To account for this, you can add a ```chunk_prefix```:
```python
nlp = spacy.load("en_core_sci_sm")
ts = termset("en_clinical")
nlp.add_pipe(
"negex",
config={
"chunk_prefix": ["no"],
},
last=True,
)
doc = nlp("There is no headache.")
for e in doc.ents:
print(e.text, e._.negex)
# no headache True
```
## Contributing
[contributing](https://github.com/jenojp/negspacy/blob/master/CONTRIBUTING.md)
## Authors
* Jeno Pizarro
## License
[license](https://github.com/jenojp/negspacy/blob/master/LICENSE)
## Other libraries
This library is featured in the [spaCy Universe](https://spacy.io/universe). Check it out for other useful libraries and inspiration.
If you're looking for a spaCy pipeline object to extract values that correspond to a named entity (e.g., birth dates, account numbers, or laboratory results) take a look at [extractacy](https://github.com/jenojp/extractacy).
%package help
Summary: Development documents and examples for negspacy
Provides: python3-negspacy-doc
%description help
# negspacy: negation for spaCy
[](https://dev.azure.com/jenopizzaro/negspacy/_build/latest?definitionId=2&branchName=master) [](https://spacy.io) [](https://pypi.org/project/negspacy/) [](https://zenodo.org/badge/latestdoi/201071164) [](https://github.com/ambv/black)
spaCy pipeline object for negating concepts in text. Based on the NegEx algorithm.
***NegEx - A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries
Chapman, Bridewell, Hanbury, Cooper, Buchanan***
[https://doi.org/10.1006/jbin.2001.1029](https://doi.org/10.1006/jbin.2001.1029)
## What's new
Version 1.0 is a major version update providing support for spaCy 3.0's new interface for adding pipeline components. As a result, it is not backwards compatible with previous versions of negspacy.
If your project uses spaCy 2.3.5 or earlier, you will need to use version 0.1.9. See [archived readme](https://github.com/jenojp/negspacy/blob/v0.1.9_spacy_2.3.5/README.md).
## Installation and usage
Install the library.
```bash
pip install negspacy
```
Import library and spaCy.
```python
import spacy
from negspacy.negation import Negex
```
Load spacy language model. Add negspacy pipeline object. Filtering on entity types is optional.
```python
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("negex", config={"ent_types":["PERSON","ORG"]})
```
View negations.
```python
doc = nlp("She does not like Steve Jobs but likes Apple products.")
for e in doc.ents:
print(e.text, e._.negex)
```
```console
Steve Jobs True
Apple False
```
Consider pairing with [scispacy](https://allenai.github.io/scispacy/) to find UMLS concepts in text and process negations.
## NegEx Patterns
* **pseudo_negations** - phrases that are false triggers, ambiguous negations, or double negatives
* **preceding_negations** - negation phrases that precede an entity
* **following_negations** - negation phrases that follow an entity
* **termination** - phrases that cut a sentence in parts, for purposes of negation detection (.e.g., "but")
### Termsets
Designate termset to use, `en_clinical` is used by default.
* `en` = phrases for general english language text
* `en_clinical` **DEFAULT** = adds phrases specific to clinical domain to general english
* `en_clinical_sensitive` = adds additional phrases to help rule out historical and possibly irrelevant entities
To set:
```python
from negspacy.negation import Negex
from negspacy.termsets import termset
ts = termset("en")
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(
"negex",
config={
"neg_termset":ts.get_patterns()
}
)
```
## Additional Functionality
### Change patterns or view patterns in use
Replace all patterns with your own set
```python
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(
"negex",
config={
"neg_termset":{
"pseudo_negations": ["might not"],
"preceding_negations": ["not"],
"following_negations":["declined"],
"termination": ["but","however"]
}
}
)
```
Add and remove individual patterns on the fly from built-in termsets
```python
from negspacy.termsets import termset
ts = termset("en")
ts.add_patterns({
"pseudo_negations": ["my favorite pattern"],
"termination": ["these are", "great patterns", "but"],
"preceding_negations": ["wow a negation"],
"following_negations": ["extra negation"],
})
#OR
ts.remove_patterns(
{
"termination": ["these are", "great patterns"],
"pseudo_negations": ["my favorite pattern"],
"preceding_negations": ["denied", "wow a negation"],
"following_negations": ["unlikely", "extra negation"],
}
)
```
View patterns in use
```python
from negspacy.termsets import termset
ts = termset("en_clinical")
print(ts.get_patterns())
```
### Negations in noun chunks
Depending on the Named Entity Recognition model you are using, you _may_ have negations "chunked together" with nouns. For example:
```python
nlp = spacy.load("en_core_sci_sm")
doc = nlp("There is no headache.")
for e in doc.ents:
print(e.text)
# no headache
```
This would cause the Negex algorithm to miss the preceding negation. To account for this, you can add a ```chunk_prefix```:
```python
nlp = spacy.load("en_core_sci_sm")
ts = termset("en_clinical")
nlp.add_pipe(
"negex",
config={
"chunk_prefix": ["no"],
},
last=True,
)
doc = nlp("There is no headache.")
for e in doc.ents:
print(e.text, e._.negex)
# no headache True
```
## Contributing
[contributing](https://github.com/jenojp/negspacy/blob/master/CONTRIBUTING.md)
## Authors
* Jeno Pizarro
## License
[license](https://github.com/jenojp/negspacy/blob/master/LICENSE)
## Other libraries
This library is featured in the [spaCy Universe](https://spacy.io/universe). Check it out for other useful libraries and inspiration.
If you're looking for a spaCy pipeline object to extract values that correspond to a named entity (e.g., birth dates, account numbers, or laboratory results) take a look at [extractacy](https://github.com/jenojp/extractacy).
%prep
%autosetup -n negspacy-1.0.3
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-negspacy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 1.0.3-1
- Package Spec generated