My thesis on Entity Linking in LaTeX

Exploratory data anlaysis and machine learning modelling detecting for duplicate question pairs.

Extending conceptual thinking with semantic embeddings.

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)

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.

We have implemented, expanded and reviewed the paper “Sense2Vec - A Fast and Accurate Method For Word Sense Disambiguation In Neural Word Embeddings" by Andrew Trask, Phil Michalak and John Liu.

Analyze fire accident factor applying word embedding and network analysis

Fake news Classifier using LSTM- NLP

Delightful Dining Discoveries: A Journey of Culinary Exploration

The primary objective of this project is to develop a robust system capable of accurately classifying patient conditions solely based on their reviews. By leveraging advanced NLP techniques, the project aims to streamline the categorization process and provide valuable insights into patient health status.

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.