Spanish word embeddings computed with different methods and from different corpora

Extracting Skills from resume using Machine Learning

The backed for an anime recommender system that combines multiple methods to provide a variety of recommendations to users based on different similarity metrics

Automatic library of congress classification, using word embeddings from book titles and synopses.

Information retrieval course project

Sentiment Analysis on Loksabha Elections 2019

create the search engine to retrieve the text documents. (the information retrieval course project)

M.Sc. mini project for NLP class (M908)

Text classification is the task of assigning a set of predefined categories to free text. Text classifiers can be used to organize, structure, and categorize pretty much anything. For example, new articles can be organized by topics, support tickets can be organized by urgency, chat conversations can be organized by language, brand mentions can …

Bengali word embedding using BengaliWord2Vec from BNLP. A mini project under the mentorship of Prof. Sandipan Ganguly, HIT-K.

Checkout my adventures into NLP here.

This is final project of Information Retrieval course which is implementation of a search engine

An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks

Automatic library of congress classification, using word embeddings from book titles and synopses.

Arabic Word Embedding models SkipGram, and GLoVE are trained over Arabic Wiki data Dump 2018 dataset from scratch using Gensim and GLoVE python libraries. Then the models are evaluated on three NLP tasks and its results are visualized in T-SNE

The application of representation learning for semantic associated heterogeneous networks in creating android app embedding layers.

[FR - Duo] 2023 - 2024 Centrale Méditerranée AI Master | NLP project about embeddings and word2vec algorithm

Example of how to use word embedding with BlazingText algorithm in Amazon SageMaker on entire contents of wikipedia for a foreign language (Hebrew).

In this project we will be building a text classifier using LSTM and Wor2vec

Comparison of contextual (BERT) and uncontextual (GloVe and Word2Vec) word embeddings in the task of music genre classification from lyrics.