Interpreting Information in Text by Humans and Machines - Period 2

This module addresses the process of systematic text analysis through human annotation and automatic analysis using text mining techniques. Annotations make information that is implicit in data explicit, allowing researchers to explore their data, identify patterns and answer various research questions in a methodologically sound way. 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 and social science as a discipline. The degree to which annotators agree or disagree (the so-called Inter Annotator Agreement) tells us something about the reproducibility of the interpretation process, the matureness of theoretical notions and the criteria used to apply them to real data. Annotated data can be used to evaluate text mining techniques that automatically identify the same or similar information. How do these techniques work? Can a machine do better than humans? Is it possible to use the automatic annotations to extract useful informations form the text and to answer the research questions.

Humanities scholars and social scientists learn to represent their interpretation of texts in a data structure. Computer science students will learn about how text mining technologies can be applied in Humanities and Social Sciences. Different backgrounds of annotators will lead to different types of annotations. Linguists, (cultural-)historians, social-scientists, and literature-scientists will consider sources and data differently and consequently come to different annotations of the same source/data.

Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course therefore, CEA's recommended credits are based on the ECTS credits assigned by VU Amsterdam. 1 ECTS equals 28 contact hours assigned by VU Amsterdam.