This project attempts to recreate a version of the game Semantle, a variant of the five-letter word guessing game Wordle that gives the semantic similarity of the player's guess to the secret word of the day. Our version of Semantle allows the player to choose from the following pretrained word embeddings:
All the scripts are placed inside a Jupyter notebook, which also includes a detailed write-up covering the following:
This notebook was created using Google Colab and invokes commands such as gdown
and wget
. The memory requirement of loading pretrained word embeddings may also be heavy for some local machines. Therefore, we recommend running the notebook on Colab.
This is a major course output in an introduction to natural language processing class under Mr. Edward P. Tighe of the Department of Software Technology, De La Salle University.
This project is a Jupyter notebook, with the following Python libraries and modules used:
Library/Module | Description | License |
---|---|---|
gensim |
Provides functions for training vector embeddings, topic modelling, document indexing, and similarity retrieval with large corpora | GNU Lesser General Public License v2.1 |
regex |
Provides additional functionality over the standard re module while maintaining backwards-compatibility |
Apache License 2.0 |
numpy |
Provides a multidimensional array object, various derived objects, and an assortment of routines for fast operations on arrays | BSD 3-Clause "New" or "Revised" License |
io |
Provides Python's main facilities for dealing with various types of I/O | Python Software Foundation License |
random |
Provides functions for generating pseudo-random numbers with various common distributions | Python Software Foundation License |
The descriptions are taken from their respective websites.
Mark Edward M. Gonzales
[email protected]
[email protected]
Hylene Jules G. Lee
[email protected]
[email protected]
Phoebe Clare L. Ong
[email protected]
[email protected]