Deep Interpretation and Analysis by Humans and Machines

Course code:
Period 2
Language of tuition:
Faculteit der Geesteswetenschappen
dr. A.S. Fokkens
dr. A.S. Fokkens
drs. E. Maks
dr. L.M. Aroyo
prof. dr. P.T.J.M. Vossen
dr. A.S. Fokkens
T. Caselli
Teaching method(s):
Lecture, Seminar

Course objective

In this course, students will learn about the process of identifying and
annotation information in historic and contemporaneous texts such as
novels, lyrics, letters, news paper articles, movie scripts, blogs and
other other social media texts using manual and automatic methods. They
will learn the implications for the theoretical models and concepts they
are familiar with in their own discipline. Students will choose their
own texts and annotate them interdisciplinary using different tools and
methods. They will apply expert and crowd annotations, develop
code-books and compare the results. Finally, they will use a
machine-learning program for analyzing text and reflect on the
performance of the automatic annotation. We will focus on high-level
semantic annotations of, for example, (historic) events, entities and
emotions that are of interest to a broader range of humanities and
social and computer science students. They will present their findings
in a research paper.

Course content

This module addresses the human and automatic annotation of humanities
sources and data. Annotations make information that is implicit in data
explicit allowing researchers to search their data systematically. This
kind of research forces humanities researchers to represent their
interpretation of sources in a data structure. Computer science students
will learn about how text mining technologies can be applied in
Humanities and Social Sciences. Annotation requires the use of some type
of interpretation model and it results in an analysis that can be
compared across annotators. As such, annotation can be seen as an
important step towards the formalization of humanities as a discipline.
The degree to which annotators agree or disagree (the so-called Inter
Annotator Agreement) tells us something about the eproducibility of the
interpretation process, the matureness of theoretical notions and the
criteria used to apply them to real data. Different backgrounds of
annotators will lead to different types of annotations. Linguists,
(cultural- )historians, social-scientists, literature-scientists will
consider sources and data differently and consequently come to different
annotations of the same source/data. The same holds for experts and
non-experts. The former are traditionally involved in assigning metadata
to sources, the latter do the same in crowd-sourcing initiatives.
Finally, annotated data can be used to train machines to do the same.
How does this work? Can a machine do better than humans? How do you
evaluate this?

Form of tuition

Lectures (2 hrs per week) and work groups (2hrs per week)

Type of assessment

Presentations and intermediate assignments (20%) Final Paper (80%). The
final paper must have a passing grade on its own. Presentations and
intermediate assignment may be compensated by the final paper.

Course reading

Course reader

Recommended background knowledge

Recommended (but not required) background knowledge: minor course 2:
From Object to Data

Target audience

Students of the UvA & VU faculty of Humanities and Social Sciences, as
well as students of Informatics (UvA) and Computer Science (VU)


This module is taught at the VU. Module registration at the VU is

© Copyright VU University Amsterdam
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