Jordan graduated from NYU in 2011 with a degree in East Asian studies (Chinese). Since then, he has founded startups in both education and language technology (for example, https://www.wordsurge.com). In 2016, Jordan began a PhD in cognitive and information science at UC Merced, where he focuses on computational phonology, particularly verbal art. He is currently an NSF NRT Fellow in intelligent adaptive systems and an NSF GRFP Fellow in natural language processing. He enjoys running, surfing, chess, rap and tinkering.
The Space of Perfect Rhyme and Perfect Similarity »
Project: Unsupervised Rhyme Detection: Without Pronunciation
Goal of the project:
The goal of this project is to identify rhyming word pairs from poetry without phonological information and in an unsupervised manner, specifically with clustering/graph theory. We attempt to effectively cluster groups of rhymes in an effort to discover how words may have been pronounced in the past.
Summary of work:
Rhyming and non-rhyming data was collected. Unlabeled rhyming poems were processed and inserted into a graph with words as nodes and observed pairs as edges. Pairs of words are clustered using graphs, and edges are weighted and cut if they do not meet a frequency threshold.
Results and future work:
The resulting graphs eliminate many non-rhymes and show relatively dense clusters of rhymes. In future work, we plan to utilize the non-rhyming from Wikipedia to further prune these graphs of high frequency non-rhyming pairs. This should allow for cutting of pairs such as “sky-day” and “i-me” from the graph.