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6 changes: 6 additions & 0 deletions .flake8
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[flake8]
max-line-length = 80
extend-select = B950
extend-ignore = E203,E501,E701
per-file-ignores =
quaxa/__init__.py:F401
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name: Python application

on: [push]

jobs:
build:
strategy:
matrix:
platform: [windows-latest, macos-latest, ubuntu-latest]

runs-on: ${{ matrix.platform }}

steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.x"
- name: Install dependencies
run: |
pip install -U pip
pip install '.[dev]'
- name: Run unit tests
run: |
pytest --cov=quaxa
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.vscode
profile/data*
.theia
*.temp
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# See https://pre-commit.com for more information
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.2.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- id: check-added-large-files
- repo: https://github.com/psf/black
rev: 22.10.0
hooks:
- id: black
- repo: https://github.com/PyCQA/flake8
rev: 7.0.0
hooks:
- id: flake8
additional_dependencies: [flake8-bugbear]
- repo: https://github.com/pycqa/isort
rev: 5.12.0
hooks:
- id: isort
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v1.8.0
hooks:
- id: mypy
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distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
limitations under the License.
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156 changes: 69 additions & 87 deletions README.md
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[![PyPI version](https://badge.fury.io/py/quaxa.svg)](https://badge.fury.io/py/quaxa)
[![PyPi downloads](https://img.shields.io/pypi/dm/quaxa)](https://img.shields.io/pypi/dm/quaxa)
[![DOI](https://zenodo.org/badge/667310199.svg)](https://zenodo.org/badge/latestdoi/667310199)
# quaxa - QUAlity of sentence eXAmples

# QUAXA: QUAlity of sentence eXAmples scoring
Rule-based sentence scoring algorithm based on GDEX.
_Rule-based sentence scoring algorithm based on GDEX_

This package provides a
[GDEX](https://www.sketchengine.eu/guide/gdex/)-based algorithm for
evaluating sentences with regard to their suitability as good examples
in dictionaries. It applies a numeric score between zero and one to
sentences which have been preprocessed with the NLP tool
[spaCy](https://spacy.io/). The score is computed by taking several
configurable criteria into account, firstly knock-out criteria which
have to be fulfilled in order to reach a score above zero at all, as
well as gradual criteria that factor into a score greater than zero.

## Rules
Among the knock-out criteria are

### Formel
* the character set of a sentence not containing any invalid ones (i. e. control characters),
* properly parsed sentences with punctuation at the end, and
* the existence of a finite verb and a subject, annotated and related
in a sentence's dependency parse tree.

```
score = 0.5 * isnoknockout + 0.5 * gesamtfaktor
```

### Knock-out Kritieren
Wenn 1 Knock-out Kriterium identifiziert wird, dann wird direkt der Score direkt um 0.5 (von 1.0) gesenkt.

| Funktion | Ausgabe | Beschreibung | Hinweis |
|:---:|:---:|:---|:---|
| `has_finite_verb_and_subject` | bool | Der Satz hat ein finites Verb und ein Subjekt, wovon eines Root des Dependenzbaum ist oder via beide über Root Node verknüpft sind. | [1] GDEX whole sentence |
| `is_misparsed` | bool | Das 1. Zeichen des Strings ist kleingeschrieben, ein Leerzeichen oder eine Punktuation; oder das letzte Zeichen ist keine Punktuation. | [1] GDEX whole sentence |
| `has_illegal_chars` | bool | String enhält Kontrolzeichen (ASCII 0-31), oder die Zeichen `<>|[]/\^@'` (z.B. HTML Tags, Markdown Hyperlinks, Dateipfade, E-Mail, u.a.) | [1] GDEX illegal chars |
| `has_blacklist_words` | bool | Satzbeleg enthält Wörter, sodass in keinem Fall der Satzbeleg als Wörterbuchbeispiel in Betracht gezogen wird; ausgenommen das Blacklist-Wort ist selbt der Wörterbucheintrag. (dt. Blacklist ist voreingestellt) | [1] GDEX blacklist |

### Diskontierungsfakoren
Je Kriterium wird ein Faktor berechnet, und alle Faktoren miteinander multipliziert.
Wenn bspw. ein Faktor eine Penality von 0.1 bekommt, dann ist der Faktor 0.9.
Für den Gesamtscore wird der Gesamtfaktor mit 0.5 multipliziert.
Among the gradual criteria are

| Funktion | Ausgabe | Beschreibung | Hinweis |
|:---:|:---:|:---|:---|
| `factor_rarechars` | [0.0, 1.0] | Strafe für jedes Zeichen, was `0123456789\'.,!?)(;:-` ist (Zahlen bzw. lange Zahlen; `.` für Abk.; mehrere Punktuationen; Bindestrichwörter, u.a.) | [1] GDEX rare chars |
| `factor_notkeyboardchar` | [0.0, 1.0] | Der Prozentsatz der Zeichen, die mit einem deutsche Tastaturlayout getippt werden können. | n.a. |
| `factor_graylist_words` | [0.0, 1.0] | Strafe Satzbelege ab, wenn Lemma auf einer Graylist steht; ausgenommen das Graylist-Wort ist selbt der Wörterbucheintrag. (Default: Keine Graylist voreingestellt) | [1] GDEX greylist |
| `factor_named_entity` | [1.0 - penalty, 1.0] | Strafe Satzbeleg ab, wenn Lemma ein o. Teil eines Eigennamen ist | [1] GDEX upper case (rare chars), [2] GBEX NE |
| `deixis_time` | [0.0, 1.0] | Strafe Signalwörter für Temporaldeixis ab. | [2] GBEX Dexis; [3] |
| `deixis_space` | [0.0, 1.0] | Strafe Signalwörter für Lokaldeixis ab. | [2] GBEX Dexis; [3] |
| `deixis_person` | [0.0, 1.0] | Strafe Wörter mit `UPOS=PRON` und `PronType=Prs|Dem|Ind|Neg|Tot` ab. Entspricht STTS PoS-Tags `PDS` (`PRON` + `Dem`, z.B, das, dies, die, diese, der), `PIS` (`PRON` + `Ind|Neg|Tot`, z.B, man, allem, nichts, alles, mehr), `PPER` (`PRON` + `Prs`, z.B, es, sie, er, wir, ich), `PPOSS` (`PRON` + `Prs`, z.B, ihren, Seinen, seinem, unsrigen, meiner). | [1] GDEX graylist PoS- Tags, [2] GBEX Dexis; [3], [4] |
| `optimal_interval` | [0.0, 1.0] | Strafe Satzbelege mit zu wenigen/vielen Wörter ab ab. | [1] GDEX |
* the absence of blacklisted words (i. e. vulgar or obscene),
* the absence of rare characters or those normally not available on a keyboard,
* the absence of named entities,
* the absence of deictic expressions,
* an optimal length of the sentence, and
* a whitelist-based coverage test, i. e. for penalizing usage of rare lemmata.

## Installation

Quellen:
- [1] Lexical Computing, "GDEX configuration introduction", URL: https://www.sketchengine.eu/syntax-of-gdex-configuration-files/
- [2] Didakowski, Lemnitzer, Geyken, 2012, "Automatic example sentence ex- traction for a contemporary German dictionary", URL: https://euralex.org/publications/automatic-example-sentence-extraction-for-a-contemporary-german-dictionary/
- [3] LingTermNet, URL: https://gsw.phil-fak.uni-duesseldorf.de/diskurslinguistik/index.php?title=Deiktischer_Ausdruck
- [4] Universial Dependency, UPOS-STTS conversion table, URL: https://universaldependencies.org/tagset-conversion/de-stts-uposf.html


## Appendix

### Installation
The `quaxa` [git repo](http://github.com/ulf1/quaxa) is available as [PyPi package](https://pypi.org/project/quaxa)
`quaxa` can be installed as a package from its GitHub source repository:

```sh
pip install quaxa
pip install git+ssh://[email protected]/ulf1/quaxa.git
pip install git+https://github.com/zentrum-lexikographie/quaxa.git
```

### Install a virtual environment
For development, clone it from GitHub and install it locally, including optional dependencies:

```sh
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt --no-cache-dir
pip install -r requirements-dev.txt --no-cache-dir
``` sh
pip install -e .[dev]
```

(If your git repo is stored in a folder with whitespaces, then don't use the subfolder `.venv`. Use an absolute path without whitespaces.)

### Python commands
## Usage

* Jupyter for the examples: `jupyter lab`
* Check syntax: `flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')`
* Run Unit Tests: `PYTHONPATH=. python -m unittest`

Publish

```sh
python setup.py sdist
twine upload -r pypi dist/*
```

### Clean up

```sh
find . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venv
``` python-console
>>> import spacy, quaxa
>>> nlp = spacy.load("de_core_news_sm")
>>> [s._.quaxa for s in quaxa.de_core(nlp("Achtung! Das ist ein toller Test.")).sents]
[0.0, 0.5966]
```

## Testing

### Support
Please [open an issue](https://github.com/ulf1/quaxa/issues/new) for support.


### Contributing
Please contribute using [Github Flow](https://guides.github.com/introduction/flow/). Create a branch, add commits, and [open a pull request](https://github.com/ulf1/quaxa/compare/).
Run tests, including calculation of code coverage:

``` sh
pytest --cov=quaxa
```

### Acknowledgements
The "Evidence" project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - [433249742](https://gepris.dfg.de/gepris/projekt/433249742) (GU 798/27-1; GE 1119/11-1).

### Maintenance
- till 31.Aug.2023 (v0.1.0) the code repository was maintained within the DFG project [433249742](https://gepris.dfg.de/gepris/projekt/433249742)
- since 01.Sep.2023 (v0.1.0) the code repository is maintained by Ulf Hamster.
## Acknowledgements

This package was initially developed as part of the [EVIDENCE
project](https://gepris.dfg.de/gepris/projekt/433249742) and funded by
the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation,
GU 798/27-1; GE 1119/11-1). Between August 2023 and October 2024, it
has been maintained by [Ulf Hamster](https://github.com/ulf1/).

Quaxa makes use of [VulGer](https://aclanthology.org/W19-3513), a
lexicon covering words from the lower end of the German language
register — terms typically considered rough, vulgar, or
obscene. VulGer is used under the terms of the CC-BY-SA license.

## Bibliography

* Adam Kilgarriff, Miloš Husák, Katy McAdam, Michael Rundell and Pavel
Rychlý. [GDEX: Automatically finding good dictionary examples in a
corpus](http://www.sketchengine.co.uk/wp-content/uploads/2015/05/GDEX_Automatically_finding_2008.pdf).
In Proceedings of the 13th EURALEX International Congress. Spain,
July 2008, pp. 425–432.
* Didakowski, Jörg, Lothar Lemnitzer, and Alexander Geyken. [Automatic
example sentence extraction for a contemporary German
dictionary](https://euralex.org/publications/automatic-example-sentence-extraction-for-a-contemporary-german-dictionary/). Proceedings
EURALEX. 2012.
* Elisabeth Eder, Ulrike Krieg-Holz, and Udo Hahn. 2019. [At the Lower
End of Language—Exploring the Vulgar and Obscene Side of
German.](https://aclanthology.org/W19-3513) In Proceedings of the
Third Workshop on Abusive Language Online, pages 119–128, Florence,
Italy. Association for Computational Linguistics.
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