English

Once every semester

In the past 15 years, language technology has considerably matured driven by the massive increase of textual and spoken data and the need to process them automatically. Although there are few systems entirely dedicated to language processing, there are now scores of applications that are to some extent "language-enabled" and embed language processing techniques such as spelling and grammar checkers, information retrieval and extraction, or spoken dialogue systems. This makes the field form a new requirement for the CS engineers.

The course introduces theories used in language technology. It attempts to cover the whole field from character encoding and statistical language models to semantics and conversational agents, going through syntax and parsing. It focuses on proven techniques as well as significant industrial or laboratory applications.

An overview of language technology: disciplines, applications, and examples
Corpus and word processing: regular expressions, automata, an introduction to Perl, concordances, tokenization, counting words, collocations
Morphology and part-of-speech tagging: word morphology, transducers, part-of-speech tagging,
Phrase-structure grammars: constituents, trees, DCG rules, unification.
Partial parsing: multiword detection, noun group and verb group extraction, information extraction, evaluation
Syntax: formalisms, constituency and dependency, functions, parsing, statistical parsing, dependency parsing.
Semantics: formal semantics, lambda-calculus, lexical semantics, predicate--argument structures, frame semantics, semantic parsing.
Discourse and dialogue: reference and coreference, discourse and rhetoric, discourse relations, parsing discourse relations, dialogue automata, speech acts, multimodality.

Understand the field of language technology and major applications using them
Know the most important techniques, fundamental algorithms, and most common architectures used in applications
Create and implement language processing algorithms. Write, interpret, evaluate, and improve them during the programming laboratories.

Understand and develop annotation schemes, create and process structured documents
Understand and write regular expressions and use them in languages like Perl or Java
Use logic and a logic programming language like Prolog
Understand and use machine--learning algorithms and statistical techniques
Develop and evaluate algorithms in major fields of language technology: language models, partial parsing, dependency parsing, and semantic parsing using real data.

Show curiosity, creativity, and problem solving aptitudes
Show an understanding of industrial and research issues in language technology

Lectures

Laboratory exercises

Written exam

Failed, pass

EDAA01 Programming - Second Course

Language Processing with Perl and Prolog, Theories, Implementation, and Application. Pierre Nugues, 2014. ISBN 9783642414640.

EDAN20F

 -03-22

Professor Thomas Johansson