Python implementation of an N-gram language model with Laplace smoothing and sentence generation.

demo of domain corpus bootstrapping using language model perplexity

Protests and agitations have long used as means for showing dissident towards social, political and economic issues in civil societies. In recent years we have witnessed a large number of protests across various geographies. Not to be left behind by similar trends in the rest of the world, South Africa, in recent years have witnessed a large num…

Language Modelling, CMI vs Perplexity

Word-prediction algorithm implemented in R (with R Shiny app) then redone in a simplified Python version

Predicting the next word using n-grams

N-gram Language Model

Generating text sequences using attention-based Bi-LSTM

language modeling

Automatic Response Generation to Conversational Stimuli

In this project we will generate the sentences using ngrams

Programming for NLP Project - Implement a basic n-gram language model and generate sentence using beam search

You will learn to implement t-SNE models in scikit-learn and explain the limitations of t-SNE. Being able to abstract high-dimensional information into lower dimensions will prove helpful for visualization and exploratory analysis, as well as in conjunction with the clustering algorithms. By the end of this course, you will be able to find clus…

Practical demonstration of scikit learn library for building various classification and regression models

language modeling, bigram, trigram, mixture of language models, perplexity, Gutenberg project

The autocomplete system model for Indonesian was built using the perplexity score approach and n-grams count probability in determining the next word.

Bigram and Trigram Language Models. This repository provides my solution for the 1st Assignment for the course of Text Analytics for the MSc in Data Science at Athens University of Economics and Business.

MNIST Digit recognition using machine learning techniques