This repository delivers a possible implementation for the paper:
MaSiMe: A Customized Similarity Measure and Its Application for Tag Cloud Refactoring https://doi.org/10.1007/978-3-642-05290-3_112
The code is a Python script that leverages the Natural Language Toolkit (NLTK) and its WordNet interface to analyze and modify a given tag cloud by mapping each word to its most frequently associated synonym based on the provided data.
The script performs the following key operations:
- Initializes a sample tag cloud as a dictionary with words and their respective counts.
- Finds synonyms for each word using NLTK's WordNet.
- Maps each word in the tag cloud to its most frequent synonym.
- Aggregates the counts of the original words based on this new mapping.
- Outputs a modified tag cloud reflecting these changes.
Before running the script, ensure you have Python installed on your system. Additionally, you will need NLTK and its WordNet dataset. Follow these steps to set up:
- Install Python: Download and install Python from the official website.
- Install NLTK: Run the following command to install NLTK:
pip install nltk- Download WordNet Data: The script will automatically download the necessary WordNet data from NLTK. However, you can also manually download it using a Python interpreter:
import nltk
nltk.download('wordnet')To use the script, simply run it with a Python interpreter. You can modify the tag_cloud dictionary in the script to use your own data. The script will output the new tag cloud based on synonym mappings.
python app.py
- The script's efficiency and accuracy are dependent on the WordNet dataset's coverage.
- Synonym mappings are based on the frequency of words in the initial tag cloud, which may not always represent semantic similarity accurately.
This script is provided as-is under the MIT License. Feel free to use, modify, and distribute as needed.