about

introduction

I have recently received a masters degree in Human Language Technology from The University of Arizona. My work intersects linguistics and computer science with a professional background in healthcare. During my time at UA I have collaborated with the linguistics and computer science department to annotate novel datasets for large language models.

I am formally trained in natural language processing, machine learning, and linguistic theory. I also have worked on machine translation systems recently during my internship at XRI Global, training neural systems and performing data integration on parallel texts from Hugging Face.

academics

A large part of my experience at UA was situated outside my home department (i.e., linguistics) and in the computer science and information science department where I was trained in machine learning¹, algorithms for natural language processing², text retrieval³ and modern advancements in deep learning. My background in linguistics complemented my understanding of language technologies and motivated the use of these tools. There is both a technical aspect as well as the “bigger picture” that I am trained in.

Below are some of the natural language processing tasks, machine learning algorithms, and computer science concepts I’m versed in:

• Natural Language Processing:

  • linguistic tasks: part-of-speech tagging, dependency parsing, named entity recognition, language detection, language modeling
  • rule-based, statistical, and neural approaches
  • instrinsic & extrinsic evaluation
  • machine translation
  • information retrieval & web search
  • dataset creation (e.g., experience using kappa’s agreement)
  • static and contextualized embeddings
  • retrieval-augmented generation
  • fine-tuning pretrained language models

• Machine Learning

  • classical algorithms: linear regression, k-means, principal component analysis, logistic regression, naive bayes, support vector machines
  • the bayesian approach to machine learning
  • deep learning: multi-layer perceptron, convolutional networks, recurrent networks
  • attention (which is just the weighted average…), transformers
  • technical and conceptual understanding of algorithms
  • optimization methods and some learning theory

• Computer Science

  • solving tasks with appropriate tools (deterministic and stochastic methods)
  • space and time complexity
  • formal language theory
  • on-the-fly computation

During the program, I spent a great deal of studying mathematics for machine learning, learning best practices for software development, and the baseline methodology for extracting natural language to do intelligent tasks. I also learned the value of being an interdisciplinary individual and the importance of collaboration during my time with the computer science department. That is not to disregard my previous background in linguistics; I fancied topics such as sociolinguistics, bilingualism, syntax, and learned French. Fine, how to read in French.

resources

This is a collection of useful readings⁴.

footnotes