Course Materials (along with assignments) for Intro to NLP, done as a part for requirement of the course "Introduction to NLP" (course-code: CS7.401.S22) @ IIITH. Note: If you are cloning this or taking help of this repo, try to star the repo.

This repository implements N-gram language modeling with Kneser-Kney and Witten Bell smoothing techniques, including an in-house tokenizer. It also features a neural model with LSTM architecture and calculates perplexities for comparing language and neural models.

Kneyser Ney and Witten Bell Language Models

Python implementation of 4-gram language models that use Witten-Bell, Kneser-Ney Smoothing

Implemented and compared Statistical and Neural Language Models. Built N‑gram models with Kneser‑Ney & Witten‑Bell smoothing and a Neural Language Model, evaluated using sentence‑level and corpus‑level perplexity scores on multiple text corpora to analyze performance differences between statistical and neural approaches