semvec/pypi-packages huggingface dataset:
Get-Wordsense 1.0.1
This module is used to get Wordsense of ambiguous word
Keywords: WSD,Sense,Disambiguation,Lesk,word-sense,stem,Knowlege based,wordnet,NLP

======================
Get_Wordsense
======================

The whole idea of word sense is controversial.
The meaning of a word is highly contextual and depends on its usage in sentence.

English is very difficult language to learn by robot as
a lot of words are Ambiguous( Word with diffrent meanings).

To solve this state-of-the-art problem, we have implemented the solution using Knowledge-Based Method ,
which concerned with identifying which sense of a word is used in a sentence.

# How to Install

```python
$ python
>>>pip install -U nltk
>>>nltk.download('stopwords')
>>>pip install Get_Wordsense
```

# Demonstration

```python
$ python
>>>sentence = 'I went to the bank to deposit my money'
>>>ambiguous_word = 'bank'
>>>print Get_sense(sentence, ambiguous_word, pos=None, synsets=None)
'a financial institution that accepts deposits and channels the money into lending activities'
```

# Cite

To cite `Get_Wordsense`:

Anant Dashpute. 2021. Get_Wordsense: Python Implementation of Get_Wordsense.
Retrieved from <https://github.com/DASHANANT/Get_Wordsense>


# References

- Michael Lesk. 1986. Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In Proceedings of the 5th annual international conference on Systems documentation (SIGDOC '86), Virginia DeBuys (Ed.). ACM, New York, NY, USA, 24-26. DOI=10.1145/318723.318728 http://doi.acm.org/10.1145/318723.318728

- Zhi Zhong and Hwee Tou Ng. 2010. It makes sense: a wide-coverage word sense disambiguation system for free text. In <i>Proceedings of the ACL 2010 System Demonstrations</i> (<i>ACLDemos '10</i>). Association for Computational Linguistics, USA, 78–83.

# Suggestions

Feel free to drop your suggestion at the following email address<br/>

> Author: Anant Dashpute<br/>
> Email: <anantdashpute@gmail.com>
datascienv 0.1.2
Data Science package for setup data science environment in single line
Keywords: Datascienv,Data Science installation in single line,data science,datascience,datasci,datascience environment

# Data Science Environment Setup in single line

#  [![PyPI version](https://badge.fury.io/py/datascienv.svg)](https://badge.fury.io/py/datascienv) [![GitHub version](https://badge.fury.io/gh/ashishpatel26%2Fdatascienv.svg)](https://badge.fury.io/gh/ashishpatel26%2Fdatascienv) [![PyPI license](https://img.shields.io/pypi/l/ansicolortags.svg)](https://pypi.python.org/pypi/ansicolortags/) [![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/) 

This package helps to setup your Data Science environment in single line.

Developed by Ashish Patel(c) 2020.

## datascienv

datascienv is a python package offering a single line Data Science Environment setup. 


### Installation

---

#### Dependencies

`datascienv` is tested to work under Python 3.7+ and greater. The dependency requirements are based on the `datascienv` package update release:

- `pandas`(latest) - https://pandas.pydata.org/
- `numpy`(latest) - https://numpy.org/install/
- `scipy`(latest) - https://www.scipy.org/
- `scikit-learn`(latest) - https://scikit-learn.org/
- `joblib`(latest) - https://joblib.readthedocs.io/en/latest/
- `statmodels`(latest) - https://www.statsmodels.org/stable/index.html
- `matplotlib`(latest) - https://matplotlib.org/
- `seaborn`(latest) - https://seaborn.pydata.org/
- `xgboost`(latest) - https://xgboost.ai/sponsors
- `imbalanced-learn`(latest) - https://imbalanced-learn.org/
- `bokeh`(latest) - https://docs.bokeh.org/en/latest/
- `Boruta`(latest) - https://github.com/scikit-learn-contrib/boruta_py
- `jupyter`(latest) - https://jupyter.org/
- `spyder`(latest) - https://www.spyder-ide.org/
- `mlxtend`(latest) - http://rasbt.github.io/mlxtend/
- `lightgbm`(lightgbm) - https://lightgbm.readthedocs.io/en/latest/
- `catboost`(latest) - https://catboost.ai/
- `pycaret`(latest) - https://pycaret.org/


#### Installation

* datascience is currently available on the PyPi's repository and you can install it via pip:

```bash
pip install -U datascienv
```

* If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:

```bash
git clone https://github.com/ashishpatel26/datascienv.git
cd datascienv
pip install .
```

* Or install using pip and GitHub:

```bash
pip install -U git+https://github.com/ashishpatel26/datascienv.git
```

* **Warnings:** If you find this type of warning then ignore that warning.

![](https://raw.githubusercontent.com/ashishpatel26/datascienv/main/img/warning.jpg)

---