Determining what words mean is the core skill and practice of lexicography. Determining what words mean is also a central challenge in natural language processing (NLP), where it is usually classed under the exercise of word sense disambiguation (WSD). Until the late 20th century, lexicography was dominated by scholars with backgrounds in philosophy, literature, and other humanistic disciplines, and the writing of dictionaries was based strongly on intuition, and only secondarily on induction from the study of examples of usage. Linguistics, in this same period, establish itself as a discipline with strong scientific credentials. With the development of corpora and other computational tools for processing text, dictionary makers recognized first the value, and soon the indispensability, of using evidence-based data to develop dictionary definitions, and this brought them increasingly into contact with computational linguists. The developers of computational linguistic tools and resources eventually turned their attention back to the dictionary and found that it was a document that could be exploited for use in the newly emerging fields of linguistic inquiry that computation made possible: NLP, artificial intelligence, machine learning, and machine translation. This course will explore the computational tools that lexicographers use today to write dictionaries, and the ways in which computational linguists use dictionaries in their pursuits. The aim is to give students an appreciation of the unexploited opportunities that dictionary databases offer to NLP, and of the challenges that stand in the way of their exploitation. Students will have an opportunity to explore the ways in which dictionaries may aid or hinder automatic WSD, and they will be encouraged to develop their own models for the use of dictionary databases in NLP.
Students must have native-speaker fluency in English. Thorough knowledge of Englsih grammar and morphology is an advantage, as is knowledge of the rudiments of NLP.