ejackson1

Image
picture of Eric Jackson
ejackson1@arizona.edu
Office Hours
Office Hours: Thursdays, 10:00 am - 12:00 pm, Zoom
Jackson, Eric
Assistant Professor

Home Department: Linguistics

SLAT Areas of Specialization: Linguistic Dimensions of L2 Learning, Technology in Second Language Teaching

Eric is a "full stack" linguist with experience in fieldwork, language documentation, and language technology. In this context, "full stack" means that he's comfortable working in any domain from phonetics to semantics—a situation often beneficial when working with communities who speak under-documented languages.

Eric studied both physics and linguistics as an undergraduate at the U of A and went on to graduate study in linguistics at UCLA. Since completing a PhD in Linguistics in 2005, he has worked in southern China and Southeast Asia in community-based applied linguistics for SIL, an international language development NGO. This work included cooperative projects with government agencies, minority language community members, and curriculum development and teaching in a joint Masters program in Kunming.

Eric is now teaching in the Masters in Human Language Technology program in the Department of Linguistics, helping students gain the skills to use computational tools for applications within natural language. Although many current natural language tools have been developed for high-resource languages like English, Eric's passion is to see these computational tools developed for language communities without huge existing datasets.

Research Interests

  • Bringing Natural Language Processing tools and techniques to low-resource language communities
  • Producing quality materials documenting threatened languages, and training & assisting others—especially language community members—to do the same
  • Lexical semantics of verbs: argument structure, event structure, and their morphological expression and manipulation
  • Serial verb constructions in Zhuang and their connection to properties of discourse
  • Using novel computational tools (vowel space density, phonemic string-edit distance) to aid traditional dialectology, especially in Tai languages of China
Area of Specialization
Linguistic dimensions of L2 learning
Technology in Second Language Teaching (minor)

Currently Teaching

LING 539 – Statistical Natural Language Processing

This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models. Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.

This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models. Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.

This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models. Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.

LING 531 – Human Language Technology II

Human Language Technology II
Course Description (no char. limit): This intermediate-level course is a continuation of LING 529 and covers a combination of theoretical and applied topics such as (but not limited to) unsupervised learning (clustering), decision trees, and the basics of information retrieval.

Human Language Technology II
Course Description (no char. limit): This intermediate-level course is a continuation of LING 529 and covers a combination of theoretical and applied topics such as (but not limited to) unsupervised learning (clustering), decision trees, and the basics of information retrieval.