Honors Theses

Date of Award

2016

Document Type

Undergraduate Thesis

Department

Modern Languages

First Advisor

Allison Burkette

Relational Format

Dissertation/Thesis

Abstract

This paper takes a deep dive into a particular area of the interdisciplinary domain of Computational Linguistics, Part-of-Speech Tagging algorithms. The author relies primarily on scholarly Computer Science and Linguistics papers to describe previous approaches to this task and the often-hypothesized existence of the asymptotic accuracy rate of around 98%, by which this task is allegedly bound. However, after doing more research into why the accuracy of previous algorithms have behaved in this asymptotic manner, the author identifies valid and empirically-backed reasons why the accuracy of previous approaches do not necessarily reflect any sort of general asymptotic bound on the task of automated Part-of-Speech Tagging. In response, a theoretical argument is proposed to circumvent the shortcomings of previous approaches to this task, which involves abandoning the flawed status-quo of training machine learning algorithms and predictive models on outdated corpora, and instead walks the reader from conception through implementation of a rule-based algorithm with roots in both practical and theoretical Linguistics. While the resulting algorithm is simply a prototype which cannot be currently verified in achieving a tagging-accuracy rate of over 98%, its multi-tiered methodology, meant to mirror aspects of human cognition in Natural Language Understanding, is meant to serve as a theoretical blueprint for a new and inevitably more-reliable way to deal with the challenges in Part-of-Speech Tagging, and provide much-needed advances in the popular area of Natural Language Processing.

Accessibility Status

Searchable text

Included in

Linguistics Commons

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.