Publication Date

Spring 2017

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Thanh D. Tran

Second Advisor

Thomas Austin

Third Advisor

Hemang Nadkarni

Keywords

named entity recognition, chatbots

Abstract

Natural language processing (NLP) is a technique by which computers can analyze, understand, and derive meaning from human language. Phrases in a body of natural text that represent names, such as those of persons, organizations or locations are referred to as named entities. Identifying and categorizing these named entities is still a challenging task, research on which, has been carried out for many years. In this project, we build a supervised learning based classifier which can perform named entity recognition and classification (NERC) on input text and implement it as part of a chatbot application. The implementation is then scaled out to handle very high-velocity concurrent inputs and deployed on two clusters of different sizes. We evaluate performance for various input loads and configurations and compare observations to determine an optimal environment.

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