Applied and Computational Linguistics

Розглядається сучасний стан прикладної та комп’ютерної лінгвістики, проаналізовано лінгвістичні теорії 20-го – початку 21-го століть під кутом розмежування різних аспектів мови з метою формалізованого опису у електронних лінгвістичних ресурсах. Запропоновано критичний огляд таких актуальних проблем прикладної (комп’ютерної) лінгвістики як укладання комп’ютерних лексиконів та електронних текстових корпусів, автоматична обробка природної мови, автоматичний синтез та розпізнавання мовлення, машинний переклад, створення інтелектуальних роботів, здатних сприймати інформацію природною мовою. Для студентів та аспірантів гуманітарного профілю, науково-педагогічних працівників вищих навчальних закладів України

An introduction to informatics (not only) for the humanities

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Gesture generation by imitation : from human behavior to computer character animation

This dissertation shows how to generate conversational gestures for an animated agent based on annotated text input. The central idea is to imitate the gestural behavior of human individuals. Using TV show recordings as empirical data, gestural key parameters are extracted for the generation of natural and individual gestures. For each of the three tasks in the generation pipeline a software was developed. The generic ANVIL annotation tool allows to transcribe gesture and speech in the empirical data. The NOVALIS module uses the annotations to compute individual gesture profiles with statistical methods. The NOVA generator creates gestures based on these profiles and heuristic rules, and outputs them in a linear script. In all, this work presents a complete work pipeline from collecting empirical data to obtaining an executable script and provides the necessary software, too.Die vorliegende Dissertation stellt einen Ansatz zur Generierung von Konversationsgesten für animierte Agenten aus annotatiertem Textinput vor. Zentrale Idee ist es, die Gestik menschlicher Individuen zu imitieren. Als empirisches Material dient eine Fernsehsendung, aus der Schlüsselparameter zur Generierung natürlicher und individueller Gesten extrahiert werden. Die Generierungsaufgabe wurde in drei Schritten mit eigens entwickelter Software gelöst. Das generische ANVIL-Annotationswerkzeug ermöglicht die Transkription von Gestik und Sprache in den empirischen Daten. Das NOVALIS-Modul berechnet aus den Annotationen individuelle Gestenprofile mit Hilfe statistischer Verfahren. Der NOVAGenerator erzeugt Gesten anhand dieser Profile und allgemeiner Heuristiken und gibt diese in Skriptform aus. Die Arbeit stellt somit einen vollständigen Arbeitspfad von empirischer Datenerhebung bis zum abspielfertigen Skript vor und liefert die entsprechenden Software-Werkzeuge dazu

Gesture generation by imitation : from human behavior to computer character animation

This dissertation shows how to generate conversational gestures for an animated agent based on annotated text input. The central idea is to imitate the gestural behavior of human individuals. Using TV show recordings as empirical data, gestural key parameters are extracted for the generation of natural and individual gestures. For each of the three tasks in the generation pipeline a software was developed. The generic ANVIL annotation tool allows to transcribe gesture and speech in the empirical data. The NOVALIS module uses the annotations to compute individual gesture profiles with statistical methods. The NOVA generator creates gestures based on these profiles and heuristic rules, and outputs them in a linear script. In all, this work presents a complete work pipeline from collecting empirical data to obtaining an executable script and provides the necessary software, too.Die vorliegende Dissertation stellt einen Ansatz zur Generierung von Konversationsgesten für animierte Agenten aus annotatiertem Textinput vor. Zentrale Idee ist es, die Gestik menschlicher Individuen zu imitieren. Als empirisches Material dient eine Fernsehsendung, aus der Schlüsselparameter zur Generierung natürlicher und individueller Gesten extrahiert werden. Die Generierungsaufgabe wurde in drei Schritten mit eigens entwickelter Software gelöst. Das generische ANVIL-Annotationswerkzeug ermöglicht die Transkription von Gestik und Sprache in den empirischen Daten. Das NOVALIS-Modul berechnet aus den Annotationen individuelle Gestenprofile mit Hilfe statistischer Verfahren. Der NOVAGenerator erzeugt Gesten anhand dieser Profile und allgemeiner Heuristiken und gibt diese in Skriptform aus. Die Arbeit stellt somit einen vollständigen Arbeitspfad von empirischer Datenerhebung bis zum abspielfertigen Skript vor und liefert die entsprechenden Software-Werkzeuge dazu

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Current trends

Deep parsing is the fundamental process aiming at the representation of the syntactic structure of phrases and sentences. In the traditional methodology this process is based on lexicons and grammars representing roughly properties of words and interactions of words and structures in sentences. Several linguistic frameworks, such as Headdriven Phrase Structure Grammar (HPSG), Lexical Functional Grammar (LFG), Tree Adjoining Grammar (TAG), Combinatory Categorial Grammar (CCG), etc., offer different structures and combining operations for building grammar rules. These already contain mechanisms for expressing properties of Multiword Expressions (MWE), which, however, need improvement in how they account for idiosyncrasies of MWEs on the one hand and their similarities to regular structures on the other hand. This collaborative book constitutes a survey on various attempts at representing and parsing MWEs in the context of linguistic theories and applications

Representation and parsing of multiword expressions

This book consists of contributions related to the definition, representation and parsing of MWEs. These reflect current trends in the representation and processing of MWEs. They cover various categories of MWEs such as verbal, adverbial and nominal MWEs, various linguistic frameworks (e.g. tree-based and unification-based grammars), various languages including English, French, Modern Greek, Hebrew, Norwegian), and various applications (namely MWE detection, parsing, automatic translation) using both symbolic and statistical approaches