INCITE Projects

April Fool's Proposal



Title: Reclaiming Joyce: Solving the NLP Problem to Understand the Writings of James Joyce

Idris H. Hsi
INCITE Lab, College of Computing
Georgia Institute of Technology

Date: April 31st
Time: 1:00 pm
Location: KACTB 243

Committee Members:

R. Daneel Olivaw
Larry P. Waterhouse
R. Chandra
Richard Daystrom
James Joyce (External)

Abstract:

The works of James Joyce, specifically Ulysses and Finnegan's Wake, have been notorious for their impenetrability due to his experimental use of language and complex narrative structures. To make Joyce more accessible to non-English Literature majors and to reduce the number of people who have to lie about having read him, we propose a translator for recompiling his text into intelligible English using a novel machine learning method for solving partially observable markov decision processes. Our application, working through a context-aware, self-adaptive user interface will return normal English phrases providing a learning scaffold for the user.

In previous work, we developed Phonix, an application that uses a combination of techniques from data mining, data clustering, and stochastic search trees in conjunction with WordNet, a dictionary based on semantic networks. By using WordNet as the domain model for the English language, our program can determine meanings for any given piece of text. Our case studies reveal it to have a 62% success rate at interpreting text in works such as Dr. Seuss's "Green Eggs and Ham" and Margaret Wise Brown's "Goodnight, Moon" and a 27% success rate at interpreting the nonsense words in Seuss's works such as 'oobleck' and 'zamp'. We do believe that this algorithm can eventually read Joyce with the secondary result of solving the NLP problem but our current implementation of the compiler is intractable (equivalent to 3-SAT). To overcome this problem,we propose to write the compiler in L-STAR, a Turing-complete language guaranteeing program termination. L-STAR has the added benefit of translating any program equivalent to 3-SAT into a polynomial time solution. To guarantee run-time efficiency, we will implement our final application on a grid-based network of quantum computers. We hope that this line of inquiry will lead to publishable results.