THE PIANO FANTASY FROM BACH TO DANIELPOUR

The Fantasy, as the term suggests, is a genre that composers have found congenial for exploring innovative and imaginative processes. Works in this genre are numerous in the solo piano literature, and extend even to works for piano and orchestra and to chamber music with piano. I was curious to explore how a specific genre of music maintained similar characteristics but evolved over time. A fantasy is primed to be inventive and I wanted to see how composers from different eras and backgrounds would handle their material in this genre. I have learned that composers worked through formal developments while making innovations within this genre. The heart of my dissertation is presented through the recording project. Because ofthe abundance ofpiano fantasies, many works had to be excluded from this project for time's sake. On two compact discs, I have recorded approximately two hours of solo piano music. I have included some shorter fantasies to magnify significant developments from era to era, country to country, and composer to composer. The first disc has recordings of eighteenth and nineteenth-century fantasies: Chromatic Fantasy and Fugue, BWV 903 by J.S. Bach (1685-1750); Fantasia inC major, H. XVII, 4 by Franz Joseph Haydn (1732-1809); Fantasy inc minor, K. 475 by Wolfgang Amadeus Mozart (1756- 1791); Fantasia inf-sharp minor, Op. 28 by Felix Mendelssohn (1809-1847); and Polonaise-Fantaisie in A-flat major, Op. 61 by Frederic Chopin (1810-1849). On the second disc I have included mid-19th, 20th and 2151-century piano fantasies: Fantasy and Fugue on the Theme B-A-C-H by Franz Liszt (1811-1886); Fantasia Baetica by Manuel de Falla (1876-1946); Three Fantasies by William Bergsma (1921-1994); Fantasy, Aria and Fugue by Frederic Goossen (1927-2011); and Piano Fantasy ("Wenn ich einmal sol! scheiden") by Richard Danielpour (b. 1956). The accompanying document includes program notes for each of the pieces recorded. They were recorded on a Steinway "D" in Dekelboum Concert Hall at the University of Maryland by Antonino D'Urzo ofOpusrite Productions. This document is available in the Digital Repository at the University of Maryland and the CO's are available through the Library System at the University of Maryland.

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.