Creates traditional word and syntactic dependency embedding models for use in finding hate speech code words.
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Updated
Dec 10, 2017
Creates traditional word and syntactic dependency embedding models for use in finding hate speech code words.
This is a NLP project to detect toxic content to improve online conversations supervised by Prof. Rudzicz @ University of Toronto.
Code and specs for CS-Embed's entry for SemEval-2020 Task-9. We present code-switched embeddings, code for code-switched bilstm sentiment classifier, and code for CS tweet collection.
Construct a "home-made" Word Embedding by building a simple Movie Review Classifier.
Development of guess a word game with NLP and optimal strategies search
Contains work done for NLP Specialization courses from DeepLearning.AI
A movies Recommender system based on NLP Techniques (WordEmbedding)
NLP Project 2 - Using ount Vector, TF-IDF Vector, Co-occurrence Matrix for Frequency based embeddings and made Word2Vec model using Continuous Bag of Words (CBOW) and Skip-Gram (SG) for Prediction based Embeddings
In this project, three different models based on GAT, GCN and SAGE have been implemented to examine their performance on two prominent social networking platforms, namely Twitter and Reddit.
Exploratory data anlaysis and machine learning modelling detecting for duplicate question pairs.
Text generation model built using Tensorflow2 and trained on TPU. The model is multilayer Bidirectional LSTM.
A repo for SemEval-2022 'Don't Patronize me' text classification with XLNET, LSTM and SVM
Code for implementation of word embeddings from scratch in python using Frequency-based Embedding(Co-occurrence Matrix method) and Prediction-based Embedding method(Word2vec method)
Extending conceptual thinking with semantic embeddings.
Code and data related to "Efficient, Compositional, Order-Sensitive n-gram Embeddings" (EACL 2017)
Opinion Extraction based on Amazon Reviews
The repository contains code to replicate the experiments in the paper "Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs", by Haiyang Zhang, Alison Sneyd and Mark Stevenson, AACL 2020.
My very first NLP project where I utilized all the concepts I learned of the topic.
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