# Nicholas Arthur FitzGerald

> PhD, University of Washington, Computer Science & Engineering, 2018

**Wikidata**: [Q113667718](https://www.wikidata.org/wiki/Q113667718)  
**Source**: https://4ort.xyz/entity/nicholas-arthur-fitzgerald

## Summary
Nicholas Arthur FitzGerald is a computer scientist who earned his PhD in Computer Science & Engineering from the University of Washington in 2018. He is known for his work on neural models for large-scale semantic role labeling, supervised by Luke Zettlemoyer.

## Biography
- Born: [date and place not specified]
- Nationality: [not specified]
- Education: PhD in Computer Science & Engineering, University of Washington (2018)
- Known for: Developing neural models for semantic role labeling
- Employer(s): [not specified]
- Field(s): Computer science, natural language processing

## Contributions
Nicholas Arthur FitzGerald's primary contribution is his doctoral thesis, *Neural Models for Large-scale Semantic Role Labelling*, completed in 2018 under the supervision of Luke Zettlemoyer at the University of Washington. This work focused on advancing neural approaches to semantic role labeling, a key task in natural language processing that involves identifying the semantic roles of words in sentences. While specific details of the thesis's findings or applications are not provided, his research aligns with the broader field of computational linguistics, which seeks to bridge human language understanding with machine processing.

## FAQs
### Q: What is Nicholas Arthur FitzGerald known for?
A: Nicholas Arthur FitzGerald is known for his doctoral research on neural models for semantic role labeling, completed in 2018 at the University of Washington.

### Q: Who was Nicholas Arthur FitzGerald's advisor?
A: Luke Zettlemoyer served as Nicholas Arthur FitzGerald's doctoral advisor.

### Q: What was the focus of Nicholas Arthur FitzGerald's PhD thesis?
A: His thesis, *Neural Models for Large-scale Semantic Role Labelling*, explored neural approaches to semantic role labeling in natural language processing.

## Why They Matter
Nicholas Arthur FitzGerald's work contributes to the ongoing development of natural language processing (NLP) technologies, particularly in semantic role labeling. His research helps improve machine understanding of human language, which has applications in areas such as information extraction, question answering, and machine translation. While his specific impact is not detailed in the provided sources, his work aligns with broader efforts to enhance computational models of language, which are foundational to many AI-driven applications. His thesis, supervised by Luke Zettlemoyer, reflects the collaborative nature of academic research in advancing NLP capabilities.

## Notable For
- Completed a PhD in Computer Science & Engineering at the University of Washington in 2018.
- Authored a thesis on neural models for semantic role labeling.
- Worked under the supervision of Luke Zettlemoyer, a researcher in computational linguistics.

## Body
### Education and Training
Nicholas Arthur FitzGerald earned his PhD in Computer Science & Engineering from the University of Washington in 2018. His doctoral work was supervised by Luke Zettlemoyer, a researcher in natural language processing. The thesis, titled *Neural Models for Large-scale Semantic Role Labelling*, focused on developing neural approaches to semantic role labeling, a critical task in NLP.

### Research Focus
FitzGerald's research contributed to the field of semantic role labeling, which involves identifying the semantic roles of words in sentences. His work likely involved designing and evaluating neural models to improve the accuracy and scalability of this task, which is essential for applications like information extraction and machine translation.

### Academic Context
The University of Washington, where FitzGerald completed his PhD, is a leading institution in computer science and engineering. His work was part of a broader academic effort to advance NLP technologies, reflecting the university's strengths in computational linguistics and AI research.

## References

1. WorldCat