Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation

An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks

This GitHub repository contains implementations of three popular word embedding techniques: Singular Value Decomposition (SVD), Continuous Bag of Words (CBOW), and Embeddings from Language Models (ELMO). Word embeddings are a fundamental component of natural language processing and are essential for various text-based machine learning tasks.

pretrained transformer and embeddings language models

An implementation of ELMo embeddings using PyTorch, featuring stacked Bi-LSTMs for contextualized word representations. Pretrained on bidirectional language modeling and evaluated on the AG News dataset for text classification, achieving metrics like accuracy, F1, and precision.

This repository contains implementations of ELMo (Embeddings from Language Models) models trained on a news dataset. Additionally, it includes a classification task using ELMo embeddings.

This project aims to implement various word embedding methods.

Intro to NLP Assignments on Word2vec, Smoothing, ELMO, POS Tagging

embeddings language models

Getting Word Embeddings using my ELMO architecture