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Filtering by: Creator Letchworth, Christopher A. Advisor Huang, Liang Committee Member Fern, Alan

Parsing with Recurrent Neural Networks

Creator:

Cross, James Henry, III

Abstract:

Machine learning models for natural language processing have traditionally relied on large numbers of discrete features, built up from atomic categories such as word forms and part-of-speech labels, which are considered completely distinct from each other. Recently however, the advent of dense feature representations coupled with deep learning techniques has...

Resource Type:

Dissertation

Full Text:

encode the entirety of a sentence in context and by proposing novel parsing systems to take advantage of
Structured learning with latent variables : theory and algorithms

Creator:

Zhao, Kai

Abstract:

Most tasks in natural language processing (NLP) try to map structured input (e.g., sentence or word sequence) to some form of structured output (tag sequence, parse tree, semantic graph, translated/paraphrased/compressed sentence), a problem known as “structured prediction”. While various learning algorithms such as the perceptron, maximum entropy, and expectation-maximization have...

Resource Type:

Dissertation

Full Text:

(tag sequence, parse tree, semantic graph, translated/paraphrased/compressed sentence), a problem
Deep Learning for Human and Biological Languages

Creator:

Hong, Junki

Abstract:

We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...

Resource Type:

Dissertation

Full Text:

language sentences have a large number of types but a limited sequence of tokens, while nucleotides in