Code and data related to "Efficient, Compositional, Order-Sensitive n-gram Embeddings" (EACL 2017)
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Updated
Apr 6, 2017 - Python
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.
Scripts for Cognitive Science Masters Thesis - Investigating Implicit Gender Bias and Stereotypes in Job Descriptions Using Word Embeddings – an Australian Context
Automatic topic modelling using minimal external input and computational resources
Contains work done for NLP Specialization courses from DeepLearning.AI
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
A movies Recommender system based on NLP Techniques (WordEmbedding)
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.
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.
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