Metadata-Version: 2.1
Name: GPLWordNet
Version: 0.0.3
Summary: A tool for generating polarity lexicons with WordNet  package
Author-email: Xuanlong QIN <xuanlongqin.cu@gmail.com>
License: MIT License
        
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Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: click >=8.1.7
Requires-Dist: joblib >=1.4.2
Requires-Dist: nltk >=3.9.1
Requires-Dist: tqdm ==4.66.5


## Introduction
GPLWordNey is a lexcion generating tool, using WordNet as database to find two sets of lexcions.

It provides:
- full expand - This function is used for finding target words' antonyms, synsets, adjectives, derivational words, and hyponyms.
- antonyms expand - This function is used for finding target words' antonyms


## Usage
### How to use it?
The are two main functions as we mentioned before, and all function are packaged in one file. Therefore, you need to call the two functions as follows, 

Method 1
```{python}
import GPLWordNet
## input your data
data = [{'term': 'good', 'PoS': 'ADJECTIVE', 'sense': 1}]

## full expand function
results = GPLWordNet.example.full_expand(data)

## Antonyms expand function
results = GPLWordNet.example.antonyms_expand(data)
```

Method 2
```{python}
from GPLWordNet import example
## input your data
data = [{'term': 'good', 'PoS': 'ADJECTIVE', 'sense': 1}]

## full expand function
results = example.full_expand(data)

## Antonyms expand function
results = example.antonyms_expand(data)
```

### Argument
Briefly iontroduce the inputs,
- datax (list of dict): A list where each dictionary contains 'term', 'PoS', and 'sense'.
  - 'term' (str): The word to expand.
  - 'PoS' (str): Part of Speech ('NOUN', 'VERB', 'ADJECTIVE').
  - 'sense' (optional): The specific sense index to use for the term.
    - 0: single words.
    - 1: synsets (More relevant words).

### Testing
Test can then be run after installation with:
```{python}
    data1 = [{'term': 'good', 'PoS': 'ADJECTIVE'}]
    print(full_expand(data1))
    
    data2 = [{'term': 'good', 'PoS': 'ADJECTIVE'},
          {'term': 'competence', 'PoS': 'Noun'}]
    print(full_expand(data2))
    
    data3 = [{'term': 'good', 'PoS': 'ADV', 'sense': 1},
          {'term': 'competence', 'PoS': 'Noun'}]
    print(antonyms_expand(data3))
```
## Supplemantal Materials
1. This work is an replication work of Nicolas' SADCAT codes, which only have R version. For more details about Nicolas Works, please refer to following link,
   - [Nicolas dictonaries Webpage](https://gandalfnicolas.github.io/SADCAT/)
   - [Nicolas GitHub](https://github.com/gandalfnicolas/SADCAT)

2. The approach I mainly learned from Maks et al.'s(2014) Paper.
   - [Generating Polarity Lexicons with WordNet propagation in five languages](http://www.lrec-conf.org/proceedings/lrec2014/pdf/847_Paper.pdf)

## Call for Contributions
The GPLWordNet project welcomes your expertise and enthusiasm!

Small improvments of fixes are always appreciated, please submit your feature according GitHUb.

Our preferred channels of communication are all public, but if you’d like to speak to us in private first, contact my public email at xuanlongqin.cu@gmail.com . 

