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Artificial Intelligence Course 4th Project: Implementing Bigram and Unigram models for filtering comments
AUT Principles and Applications of Artificial Intelligence course (Fall 2020) projects
The goal of this script is to implement three langauge models to perform sentence completion, i.e. given a sentence with a missing word to choose the correct one from a list of candidate words. The way to use a language model for this problem is to consider a possible candidate word for the sentence at a time and then ask the language model which version of the sentence is the most probable one.
NLP-persian-poet-identification
A poem will be generated using different Language models in Urdu language. This poem will consist of three stanzas each containing four verses.
Implementation of unigram/bigram language models, noisy channel and pointwise mutual information for natural language processing.
Using NLP and ML algorithm to predict the Airline Twitter Sentiment
Classifying texts using unigram and bigram models
Classifying a tweet as positive, neutral, or negative sentiment using Natural Language Processing (CBOW approaches) and Traditional Machine Learning Algorithms.
Artifacts for CS-539 Natural Language Processing course
Assignment on Document Reranking
Final AI course of CE department at Amirkabir University of Technology (Tehran Polytechnic) - Winter 2020.
Text segmentation solution using natural language processing.
Determine if a sentence is English, French or Italian.
A Survey on ML Techniques for Airbnb Price Prediction
This repository contains Natural Language Processing programs in the Python programming language.
Train 4 types of language models (a unigram model, a smoothed unigram model, a bigram model, and a smoothed bigram model) on WikiText-2, a corpus of high-quality Wikipedia articles