Foundations of Natural Language Processing (VS1) (U04284)

? Credit Points : 10 ? SCQF Level : 9 ? Acronym : INF-3-FNLP-V

This course covers some of the linguistic and algorithmic foundations of natural language processing. It builds on the material introduced in Informatics 2A and aims to equip students for more advanced NLP courses in years 3 or 4. The course is strongly empirical, using corpus data to illustrate both core linguistic concepts and algorithms, including language modeling, part of speech tagging, syntactic processing, the syntax-semantics interface, and aspects of semantic processing. Linguistic and algorithmic content will be interleaved throughout the course.

Entry Requirements

? This course is only available to part year visiting students.

? This course is a variant of the following course : U04179

? Pre-requisites : Informatics 2A

? Prohibited combinations : Advanced Natural Language Processing

Subject Areas

Home subject area

Computational Linguistics, (School of Informatics, Schedule O)

Other subject areas

Artificial Intelligence, (School of Informatics, Schedule O)

Cognitive Science, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : 3rd year

? Delivery Period : Not being delivered

? Contact Teaching Time : 3 hour(s) per week for 10 weeks

Summary of Intended Learning Outcomes

? Given an appropriate NLP problem, students should be able to select a corpus and an annotation scheme for the problem and justify the choice over other candidates. Students should also be able to identify suitable evaluation measures for the problem and provide a written explanation of the role of annotated corpora in natural language processing.
? Given one of the main linguistic issues relevant to NLP (including the representation and induction of syntactic knowledge, and the modeling of lexical and semantic information, and the syntax-semantics interface), students should be able to construct an example of the issue and provide an explanation of how thieir example illustrates the issue in general.
? Given an example of one of the main linguistic issues identified above, students should be able to classify it as belonging to that issue and relate the example to the issue in general.
? Given an NLP problem, students should be able to analyze, assess and justify which algorithms are most appropriate for solving the problem, based on an understanding of fundamental algorithms such as Viterbi algorithm, inside-outside, chart-based parsing and generation.