Text Visualization (Sommersemester 2019)
Dozent:
Prof. Dr. Ralf Krestel
(Information Systems)
,
Tim Repke
(Information Systems)
,
Nitisha Jain
(Information Systems)
,
Julian Risch
(Information Systems)
Website zum Kurs:
https://hpi.de/naumann/teaching/teaching/ss-19/text-visualization.html
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 26.04.2019
- Lehrform: Projektseminar
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
- Maximale Teilnehmerzahl: 12
Studiengänge, Modulgruppen & Module
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
Beschreibung
With the ever increasing volume of data in the modern world, data visualization has become an essential component of every data analysis task. Visualization is an effective way to convey complex information and acts as a bridge between data and decisions.
This seminar will discuss the techniques and tools for creating efficient visualizations for the most important tasks related to large textual datasets.
The seminar is geared to be a series of highly interactive sessions with the students, seeking active classroom participations. The sessions will comprise of topic introductions, brainstorming for ideas, short group activities and active discussions. To maximize the practical learning, students will be expected to submit short practical assignments for the individual topics every week.
The second part of the seminar will consist of a project to be chosen by the student teams.
Voraussetzungen
There are no prerequisites except for being open and motivated to learn about text analysis and tools to analyize and visualize large text collections.
Literatur
Collection of data visualization tools here.
Lern- und Lehrformen
Students will learn to...
- perform common text analytics tasks to explore and understand a given text dataset.
- represent large text corpora in visual form.
- use various tools to process and visualize text data.
- understand research papers and report on their own projects' progress.
Leistungserfassung
Students are supposed to do homework assignments in the first phase and to write blog posts throughout the project phase to describe and share their progress.
The final grade will be based on selected homework assignments and the weekly blog posts.
Termine
Time: Tuesday, 17:00 (moved from Monday)
Location: Campus II, F-E.06
| Date | Topic |
| 09.04. | Introduction and Organization |
| 16.04. | TFIDF/VSM + visualization |
| 23.04. | Dimensionality Reduction+PCA+tSNE+ visualization |
| 30.04. | Word Embeddings + Visualization |
| 07.05. | Topic Modeling + Visualization (ggf. am 9. oder 10.) |
| 14.05. | Text Graphs + Visualization |
| 21.05. | Knowledge Graph + Visualization |
| 28.05. | Advanced topics |
| 04.06. | Recap |
| 11.06. | Project Pitches |
| 18.06. | Projects in groups |
| 25.06. | Projects in groups |
| 02.07. | Projects in groups |
| 09.07. | Projects in groups |
| 16.07. | Projects in groups |
| TBD | Final Submission |
(subject to change)
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