Current Courses

  • Spring 2017: LING-672/COSC-672: Advanced Semantic Representation. In-depth graduate-level exploration of representations, data, and algorithms for sentence semantics, with a focus on AMR. Description

    This course will examine semantic representations for natural language from a computational/NLP perspective. Through readings, presentations, discussions, and hands-on exercises, we will put a semantic representation under the microscope to assess its strengths and weaknesses. For each representation we will confront questions such as: What aspects of meaning are and are not captured? How well does the representation scale to the large vocabulary of a language? What assumptions does it make about grammar? How language-specific is it? In what ways does it facilitate manual annotation and automatic analysis? What datasets and algorithms have been developed for the representation? What has it been used for? In Spring 2017 the focus will be on the Abstract Meaning Representation (AMR); its relationship to other representations in the literature will also be considered. Term projects will consist of (i) innovating on the representation's design, datasets, or analysis algorithms, or (ii) applying it to questions in linguistics or downstream NLP tasks.

Upcoming Courses

Past Courses

  • Fall 2016: LING-572/COSC-572: Empirical Methods in Natural Language Processing. Graduate-level survey of NLP. Description

    Systems of communication that come naturally to humans are thoroughly unnatural for computers. For truly robust information technologies, we need to teach computers to unpack our language. Natural language processing (NLP) technologies facilitate semi-intelligent artificial processing of human language text. In particular, techniques for analyzing the grammar and meaning of words and sentences can be used as components within applications such as web search, question answering, and machine translation.

    This course introduces fundamental NLP concepts and algorithms, emphasizing the marriage of linguistic corpus resources with statistical and machine learning methods. As such, the course combines elements of linguistics, computer science, and data science. Coursework will consist of lectures, programming assignments (in Python), and a final team project. The course is intended for students who are already comfortable with programming and have some familiarity with probability theory.

  • Spring 2016: INFR09028: Foundations of Natural Language Processing (University of Edinburgh School of Informatics). With Sharon Goldwater.

Scientific Tutorials

These are listed on the publications page.