Aim
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.

Knowledge and understanding
For a passing grade the student must

• 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.

Skills and abilities
For a passing grade the student must

• Understand and develop annotation schemes, create and process structured documents using XML

• 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, and dependency parsing, using real data.

Judgement and approach
For a passing grade the student must

• Show curiosity, creativity, and problem solving aptitudes

• Show an understanding of industrial and research issues in language technology

Contents

• 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.

Literature
Nugues Pierre, An Introduction to Language Processing with Perl and Prolog. An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German. Series: Cognitive Technologies, Springer Verlag, 2006, ISBN: 3-540-25031-X.