Publication Date

Fall 2022

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

William Andreopoulos

Second Advisor

Robert Chun

Third Advisor

Fabio Di Troia

Keywords

Recommender System, Machine Learning, Next Word Prediction, Natural Language Processing, AutoComplete

Abstract

With the increasing number of options or choices in terms of entities like products, movies, songs, etc. which are now available to users, they try to save time by looking for an application or system that provides automatic recommendations. Recommender systems are automated computing processes that leverage concepts of Machine Learning, Data Mining and Artificial Intelligence towards generating product recommendations based on a user’s preferences. These systems have given a significant boost to businesses across multiple segments as a result of reduced human intervention. One similar aspect of this is content writing. It would save users a lot of time if we were to come up with a way to auto-recommend words or phrases and not have to explicitly type them out. This project aims to develop a Natural Language Processing based pipeline using Machine Learning that provides next word prediction, that is, given a set of words, it will provide a list of most probable follow-up words. Moreover, we aim to form an entire sentence using intermediate next-word suggestions. The project also compares different techniques deployed to achieve the same and showcases the results on the MS COCO image-captions dataset. The results show that N-gram based probabilistic is accurately able to form an entire sentence using intermediate next-word suggestions and thus the approach performs the best on the above dataset for next word prediction.

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