Metadata-Version: 2.1
Name: ASAPPpy
Version: 0.1b2
Summary: Semantic Textual Similarity and Dialogue System package for Python
Home-page: https://github.com/NLP-CISUC/ASAPPpy
Author: José Santos
Author-email: santos@student.dei.uc.pt
License: MIT License
Keywords: Natural Language Processing,NLP,Sentence Similarity,Semantic Textual Similarity,STS,Dialogue Agents,Chatbot Framework,Chatbot
Platform: any
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.6.1
Description-Content-Type: text/markdown
Requires-Dist: setuptools (==49.6.0)
Requires-Dist: scikit-learn (==0.22.2)
Requires-Dist: pandas (>=1.1.1)
Requires-Dist: requests
Requires-Dist: slackclient (==2.1.0)
Requires-Dist: slackeventsapi (==2.1.0)
Requires-Dist: nltk (==3.4.5)
Requires-Dist: NLPyPort (==2.2.5)
Requires-Dist: spacy
Requires-Dist: gensim
Requires-Dist: joblib
Requires-Dist: num2words
Requires-Dist: Whoosh
Requires-Dist: Keras
Requires-Dist: tensorflow
Requires-Dist: cufflinks
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: imblearn

## ASAPPpy
ASAPPpy is a Python package for developing models to compute the Semantic Textual Similarity (STS) between texts in Portuguese. These models follow a supervised learning approach to learn an STS function from annotated sentence pairs, considering a variety of lexical, syntactic, semantic and distributional features.

ASAPPpy can also be used to develop STS based dialogue agents and deploy them to Slack.


### Development
If you want to contribute to this project, please follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html).


### Installation
To install the latest version of ASAPPpy use the following command:
```bash
    pip install ASAPPpy
```
After finishing the installation, you might need to download the word embeddings models. Given that they were obtained from various sources, we collected them and they can be downloaded at once by running the Python interpreter in your terminal followed by these commands:
```python
    import ASAPPpy
    ASAPPpy.download()
```
Finally, if you have never used [spaCy](https://spacy.io) before and you want to use the dependency parsing features, you will need to run the next command in the terminal:
```bash
    python -m spacy download pt
```

Alternatively, you can check the latest version of ASAPPpy using this command:
```bash
    git clone https://github.com/ZPedroP/ASAPPpy.git
```

### Project History
ASAP(P) is the name of a collection of systems developed by the [Natural Language Processing group](http://nlp.dei.uc.pt) at [CISUC](https://www.cisuc.uc.pt/home) for computing STS based on a regression method and a set of lexical, syntactic, semantic and distributional features extracted from text.
It was used to participate in several STS evaluation tasks, for English and Portuguese, but was only recently integrated into two single independent frameworks: ASAPPpy (available here), in Python, and ASAPPj, in Java.


### Help and Support

#### Documentation
Coming soon...

#### Communication
If you have any questions feel free to open a new issue and we will respond as soon as possible.

#### Citation

When [citing ASAPPpy in academic papers and theses](http://ceur-ws.org/Vol-2583/2_ASAPPpy.pdf), please use the following BibTeX entry:

    @inproceedings{santos_etal:assin2020,
        title = {ASAPPpy: a Python Framework for Portuguese STS},
        author = {José Santos and Ana Alves and Hugo {Gonçalo Oliveira}},
        url = {http://ceur-ws.org/Vol-2583/2_ASAPPpy.pdf},
        year = {2020},
        date = {2020-01-01},
        booktitle = {Proceedings of the ASSIN 2 Shared Task: Evaluating Semantic Textual Similarity and Textual Entailment in Portuguese},
        volume = {2583},
        pages = {14--26},
        publisher = {CEUR-WS.org},
        series = {CEUR Workshop Proceedings},
        keywords = {aia, asap, sts},
        pubstate = {published},
        tppubtype = {inproceedings}
    }




