LING-765 Computational Discourse Models
Fall for 2017-2018
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Recent years have seen an explosion of computational work on higher level discourse representations, such as entity recognition, mention and coreference resolution and shallow discourse parsing. At the same time, the theoretical status of the underlying categories is not well understood, and despite progress, these tasks remain very much unsolved in practice. This graduate level seminar will concentrate on theoretical and practical models representing how referring expressions, such as mentions of people, things and events, are coded during language processing. We will begin by exploring the literature on human discourse processing in terms of information structure, discourse coherence and theories about anaphora, such as Centering Theory and Alternative Semantics. We will then look at computational linguistics implementations of systems for entity recognition and coreference resolution and explore their relationship with linguistic theory. Over the course of the semester, participants will implement their own coding project exploring some phenomenon within the domain of entity recognition, coreference, discourse modeling or a related area.
Credits: 3
Prerequisites: Intermediate programming skills (e.g. in Python) are required, and a previous computational course such as Intro to NLP (LING-362) or Computational Corpus Linguistics (LING-367) is recommended.
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