Natural Language Understanding (Level 10) (U04178)

? Credit Points : 10 ? SCQF Level : 10 ? Acronym : INF-4-NLU-4

This course explores current research into interpreting natural language. Motivations for this study range from foundational attempts to understand how people interpret communication to entirely practical efforts to engineer systems for performing a variety of language tasks, such as information extraction, question answering, natural language front ends to databases, human-robot interaction and customer relationship management, to name a few.

This course represents an introduction to the theory and practice of computational approaches to natural language understanding. The course will cover common parsing methods for sentences, discourse and dialogue, and it will also address lexical processing tasks such as word sense disambiguation and clustering. We will study state of the art symbolic techniques in deep and shallow language processing, as well as statistical models, acquired by both unsupervised and supervised machine learning from online linguistic resources. Students will have the opportunity to explore what they have learned in written and practical assignments. These assignments will be designed to enable students to gain an understanding for the pervasiveness of language ambiguity at all levels and the problems this poses for automated language understanding, and for the relative strengths and weaknesses of the various theories and engineering approaches to these problems.

Entry Requirements

? Pre-requisites : Informatics 2A Foundations of Natural Language Processing Introductory Applied Machine Learning

? Prohibited combinations : Natural Language Understanding (Level 11)

Subject Areas

Home subject area

Computational Linguistics, (School of Informatics, Schedule O)

Other subject areas

Cognitive Science, (School of Informatics, Schedule O)

Artificial Intelligence, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : 4th year

? Delivery Period : Not being delivered

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	09:00	09:50	Central

Summary of Intended Learning Outcomes

• Given a parsing problem students should be able to use state-of-the-art symbolic parsing techniques, including lexicalised parsing to solve the problem and provide a written explanation of the parsing techniques used in the course.

• Given a labelled corpus, students should be able to select and use state-of-the-art statistical parsing techniques (generative and discriminative) by training parsers on the labelled corpus using existing software packages.

• Given an NLU system, students should be able to choose appropriate evaluation metrics for the system, and use error analysis to propose improvements to the language processing models.

• Given an example of a problem in coreference resolution, discourse segmentation, and discourse parsing, students should be able to provide a written description of how current symbolic and statistical techniques help solve the problem.

• Given a description of an NLU system, the student should be able to relate it to features of human models of language interpretation at various levels of processing (words, sentences, discourse and dialogue).

• Given a model and a labelled corpus, students should be able to employ existing ML software packages to train the model on the corpus in order to perform a lexical semantic task.