Sprachtechnologie in den Digital Humanities from Coursera

AKTUELL:

Am 20.05.2019 startet die vorläufig letzte Runde des MOOCs "Sprachtechnologie in den Digital Humanities". Anschliessend wird der Kurs auf der Plattform Coursera pausiert, d.h. Einschreibungen in den Kurs werden ab diesem Datum nicht mehr möglich sein. Die Videos bleiben aber nach wie vor über unseren YouTube-Kanal (https://www.youtube.com/channel/UChb3Rd5vo3WEgMSy99VInaw) bzw. den SwitchTube-Kanal der Uni Zürich (https://tube.switch.ch/channels/bb3adc02) sichtbar. Die Pausierung dient primär zur Aktualisierung und Erweiterung der Kursinhalte und Lernmaterialien.

KURS-BESCHREIBUNG:

Sie möchten wissen, was genau die Digitalisierung von Texten beinhaltet? Sie haben sich schon immer gefragt, wie Texte in einem Korpus optimal durchsuchbar gemacht werden? Sie wundern sich, wie Texte mit linguistischen Informationen angereichert werden können?

Dann sind Sie in diesem Kurs genau richtig!! Er bietet einen Überblick über die wichtigsten Konzepte und Probleme bei der Digitalisierung und Annotation von geschriebenen Texten. In sechs thematischen Modulen verteilt auf sechs Wochen lernen Sie relevante Technologien und Werkzeuge kennen. Jedes Modul beinhaltet zwei bis drei Videos (10-20 Minuten), ein Quiz oder ein Peer-Assessment sowie kurze Hintergrundtexte und weiterführende Links zu ausgewählten Themen.

Für wen ist dieser Kurs interessant:

Dieser Kurs richtet sich an Korpuslinguist/-innen, an Geisteswissenschaftler/-innen und Sprachinteressierte, die von einer rein sprachwissenschaftlichen Perspektive ausgehend auch ein paar Schritte in die Welt der Digitalisierung von Texten wagen und die dahinterstehenden Technologien kennenlernen möchten.

Für diesen Kurs brauchen Sie keine Programmierkenntnisse. Mit Interesse an der Digitalisierung und Annotation von Texten sind Sie bestens gerüstet für diesen Kurs.

Wir freuen uns, mit Ihnen diese digitalen Wege zu beschreiten, die in den Geisteswissenschaften immer wichtiger werden.

What's inside

Syllabus

Woche 1 - Wege in die digitale Welt

In der ersten Woche werden wir darüber sprechen, wie Texte digitalisiert und zum Beispiel in XML dargestellt werden können und was OCR (optical character recognition) für die Anwendung in der Praxis bedeutet. Wir befassen uns außerdem mit der Erstellung von Korpora und den Schwierigkeiten, denen wir dabei begegnen können. Viel Spaß!

Woche 2 - Strukturierte und nachhaltige Repräsentation von Korpusdaten

In der zweiten Woche beschäftigen wir uns mit der strukturierten und nachhaltigen Repräsentation von Korpusdaten. Wir werden über die Markup Language XML sprechen und einige wichtige Standards zur Textrepräsentation kennenlernen. In der zweiten Hälfte dieses Moduls geht es anschließend um das Thema der automatischen Text- und Wortsegmentierung. Viel Spaß mit den Videos!

Woche 3 - Eigenschaften von Korpora und grundlegende Analysemethoden

In der dritten Woche geht es um die wichtigsten Eigenschaften von Korpora, um grundlegende Analysemethoden in der Korpuslinguistik und Grundbegriffe wie Worthäufigkeiten, Kollokationen, N-Gramme. Im letzten Teil wird Ihnen Noah Bubenhofer noch einen Ausblick auf die visuelle und graphische Darstellung von Texteigenschaften geben. Viel Spaß!

Woche 4 - Automatische Korpusannotation mit computerlinguistischen Werkzeugen

In diesem Modul geht es um die automatische Korpus-Annotation mit linguistischen Informationen wie Part-Of-Speech-Tags (Wortarten) und Lemmas (Grundformen), aber auch um die Schwierigkeiten, die eine solche automatische Annotation mit sich bringen kann. Wir werden besprechen, wie die automatische Erkennung von Eigennamen oder geographischen Namen (Named Entities) abläuft und im dritten Teil noch auf die automatische Syntax-Analyse von Texten eingehen. Viel Spaß!

Woche 5 - Manuelle Annotation und Evaluation von Korpusdaten

In Modul 5 beschäftigen wir uns mit der Frage, wie eine möglichst ökonomische Annotation aussehen kann und wie sich manuelle und automatische Annotation mit Hilfe von maschinellem Lernen ergänzen können. Wir besprechen außerdem, wie wir die Qualität bzw. die Genauigkeit der Annotation gewährleisten und evaluieren können. Im zweiten Teil geht es dann um das sogenannte "Crowdsourcing", wo über Online-Plattformen Daten für diverse Projekte gesammelt bzw. korrigiert werden. Viel Spaß in der vorletzten Woche!

Woche 6 - Herausforderungen der Multilingualen Textanalyse

Im letzten Modul befassen wir uns mit multilingualen bzw. parallelen Korpora. Im ersten Teil geht es um die automatische Sprach-Identifikation in gemischtsprachlichen Korpora, die einen wichtigen Schritt bei der Verarbeitung der Texte darstellt. Im zweiten Teil geht es um die automatische Satz- bzw. Wortalignierung zwischen parallelen Texten in unterschiedlichen Sprachen. Viel Spaß in der letzten Woche!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by experts in the field, Simon Clematide, Martin Volk, and Noah Bubenhofer

Taught by Martin Volk, whose expertise in digital humanities is in high demand

A foundation in the basic concepts behind digital humanities will serve a variety of fields

A survey in the latest methods of computer science and digital humanities

Understanding of the latest technology and techniques

Taught by experts in the field, Noah Bubenhofer

Taught by Martin Volk, whose expertise in natural language processing is in high demand

Reviews summary

Popular linguistics course

This course is a popular choice for its modern and engaging curriculum. It provides a timely exploration of linguistics that students and learners find engaging and relevant to the field.

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Sprachtechnologie in den Digital Humanities with these activities:

Review basic concepts of linguistics and text analysis

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Strengthen your foundation by reviewing the basic concepts and principles of linguistics and text analysis.

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Refer to textbooks, online resources, or lecture notes to refresh your memory on key concepts.
Complete practice exercises or quizzes to test your understanding.

Practice using text analysis software or programming tools

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Ensure proficiency in using the necessary tools and software required for the course by practicing their application.

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Install and familiarize yourself with the software or programming tools.
Work through tutorials or sample datasets to practice using the tools effectively.

Read 'Text Mining with R: A Tidy Approach' by Julia Silge and David Robinson

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Gain a comprehensive understanding of text mining techniques in R, providing a solid foundation for the course content.

View Text Mining with R: A Tidy Approach on Amazon

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Read Chapters 1-3 to grasp the basics of text mining and R programming.
Complete the practice exercises in the book to reinforce your understanding.

Five other activities

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Complete practice exercises in the course textbook

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Reinforce your understanding of the theoretical concepts covered in the course by completing practical exercises.

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Attempt the exercises at the end of each chapter in the textbook.
Review your answers and consult the solutions provided in the book or online.

Engage in peer discussions on course topics

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Collaborate with peers to exchange ideas, clarify concepts, and enhance your understanding through group discussions.

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Join or create a study group with fellow students.
Discuss the course materials, share insights, and work through problems together.

Follow online tutorials on text analysis and corpus linguistics

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Expand your knowledge and skills by exploring additional resources and tutorials that delve deeper into specific topics.

Browse courses on Text Analysis

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Search for reputable websites or platforms offering tutorials on text analysis.
Choose tutorials that align with the topics covered in the course.
Follow the tutorials and complete any associated exercises or tasks.

Attend a workshop on advanced text analysis techniques

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Enhance your knowledge and skills by attending a workshop led by experts in the field of text analysis.

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Research and identify relevant workshops offered by universities or professional organizations.
Register for a workshop that aligns with your interests and learning goals.
Attend the workshop and actively participate in discussions and hands-on exercises.

Develop a research proposal for a text analysis project

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Apply your knowledge to a practical project by designing a research proposal for a text analysis study.

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Identify a research question and define your research objectives.
Select a suitable corpus and describe your data collection methods.
Outline the text analysis techniques you will employ and explain your rationale.
Discuss your expected outcomes and the potential significance of your research.

Career center

Learners who complete Sprachtechnologie in den Digital Humanities will develop knowledge and skills that may be useful to these careers:

Digital Humanities Specialist

Digital Humanities Specialists apply digital techniques to solve problems in human-centered fields of study. This course provides a solid grounding in the digital humanities field by introducing skills in text digitization, annotation, and visualization techniques.

See salaries and explore the career path for Digital Humanities Specialist

Natural Language Processing Engineer

Natural Language Processing Engineers build computer systems that understand, interpret, and generate human language. This course introduces Natural Language Processing by teaching fundamentals of text representation, annotation, and analysis. This course will provide Natural Language Processing Engineers with an enhanced understanding of the NLP domain.

See salaries and explore the career path for Natural Language Processing Engineer

Language Engineer

Language Engineers build natural language processing (NLP) tools that allow machines to analyze language and extract meaning. This course introduces the field of natural language processing by teaching the fundamentals of text representation, annotation, and analysis. This course will provide Language Engineers with a deeper understanding of the practical applications of NLP.

See salaries and explore the career path for Language Engineer

Text Analyst

Text Analysts leverage analytical approaches to derive meaning from text. This course provides a solid foundation in the digitization, structuring, and analysis of text. These skills are essential for Text Analysts as they need to prepare and examine text-based data.

See salaries and explore the career path for Text Analyst

Information Architect

Information Architects create frameworks that allow users to effectively find, interpret, and interact with information. This course provides a foundational understanding of the digitization, structuring, and annotation of text. These skills are essential for Information Architects as they build information structures.

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Knowledge Engineer

Knowledge Engineers leverage ontology and taxonomy to map complex information into machine understandable formats. This course provides an introduction to the representation, annotation, and structuring of knowledge, which are essential capabilities for Knowledge Engineers.

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User Experience Researcher

User Experience Researchers study user behavior to build and test user interfaces. This course may be useful as it provides a foundation in the digitization, structuring, and visualization of text. UX Researchers use these skills to create user-friendly interfaces that present text in clear, engaging ways.

See salaries and explore the career path for User Experience Researcher

Speech Scientist

Speech Scientists study acoustics, phonetics, and physiology to analyze and design speech signals. This course may be useful as it provides a foundational overview of text digitization and automatic text processing. Speech Scientists can use these skills to parse text into phonemes and digital units of sound.

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Web Developer

Web Developers design, build, and maintain websites and applications. This course may be useful as it introduces the field of digital humanities through the study of the digitization and analysis of text. Web Developers will gain knowledge of how to integrate digital text into websites and applications.

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Computational Linguist

Computational Linguists develop and apply natural language modeling to build relationships between data in order to create insights. This course may be useful as it can provide a deeper understanding of how to digitize text-based data. This course is particularly relevant because it provides exercises in applying linguistic information to text using XML.

See salaries and explore the career path for Computational Linguist

Data Analyst

Data Analysts translate data into actionable insights for business. This course may be useful as it provides a solid background in digitizing text data. This course in particular will provide foundational practices that students can build upon to develop the expertise necessary to process digital text at scale.

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Data Scientist

Data Scientists blend mathematics, statistics, and programming to gather, analyze, and interpret complex digital data for the purpose of building predictive models. This course may be useful as it provides a solid background in digitizing text data, which represents a common and important subset of the data Data Scientists work with. This course in particular will provide foundational practices that students can build upon to develop the expertise necessary to process digital text at scale.

See salaries and explore the career path for Data Scientist

Database Administrator

Database Administrators design, maintain, and manage databases to ensure efficient storage and retrieval of data. This course may be useful as it provides a foundation in the field of information architecture. As the course explores the processes used to prepare text for digital storage, learners will develop insights useful for optimizing text data storage in relational databases.

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Search Engine Optimizer

Search Engine Optimizers improve the visibility and ranking of websites in search engine results. This course may be useful as it teaches foundational methods of text digitalization, annotation, and analysis, which are key to understanding how text-based content is prepared, indexed, and ranked by search engines.

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Translator

Translators convert written text from one language to another. This course may be useful as it provides foundational knowledge in the digitization and analysis of text. This course will give Translators familiarity with the methodologies and challenges of working with digital text.

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