AdverbVec

This project utilizes word embeddings and various clustering algorithms to group adverbs based on semantic similarity, facilitating deeper insights into linguistic patterns.

About this project

AdverbVec is a Python tool for clustering and analyzing adverbs using high-dimensional embeddings. Leveraging advanced techniques, it provides a seamless way to explore the semantic relationships between adverbs in the Japanese language. With its intuitive interface and powerful features, AdverbVec empowers users to gain insights into adverb usage patterns and linguistic nuances.

Getting Started

Prerequisite

Install Python3 on your computer.

Enter this into your computer's command line interface (terminal, control panel, etc.) to check the version:

python --version

If the first number is not a 3, update to Python3.

Setup

Here is an easy way to use our GitHub repository.

Step 1: Clone the repository

Open the command line interface and run:

git clone https://github.com/ericodle/AdverbVec.git

Step 2: Navigate to the project directory

Find where your computer saved the project, then enter:

cd /path/to/project/directory

If performed correctly, your command line interface should resemble

user@user:~/AdverbVec-main$

Step 3: Create a virtual environment:

I like to use a virtual environment. Let's make one called "av-env"

python3 -m venv av-env

A virtual environment named "av-env" has been created. Let's enter the environment to do our work:

source av-env/bin/activate

When performed correctly, your command line interface prompt should look like

(av-env) user@user:~/AdverbVec-main$

Step 3: Install requirements.txt

Next, let's install specific software versions so everything works properly.

pip3 install -r requirements.txt

Step 4: Run GenreDiscern

This project has a GUI for easy use. Activate the GUI by running the following terminal command:

python3 xxx.py

function 1

Simply click

function 2

Simply click

function 3

(Feature coming soon)

Repository Files

• xxx.py

This script is xxx.

Citing Our Research

Our research paper provides a comprehensive overview of the methodology, results, and insights derived from this repository. You can access the full paper by following this link:

License

This project is open-source and is released under the MIT License. Feel free to use and build upon our work while giving appropriate credit.