|Lecturer||Dr. Christian Kahmann|
|Workload||2 SWS Lecture|
|Seminar||Thursday, 09:15 - 10:45, Paulinum P801|
|Contact||Email or Moodle|
|Intended subjects||Bachelor's, Master's or doctoral thesis in the departments TEMIR and ASV or from the fields of NLP and machine learning in general.|
- The seminar will take place in presence in room P801.
- We will try to live stream the seminar as well. The link for this is: BBB
- The first seminar is expected to take place on 7th April 2022. Here, the presentation slots will be distributed across the following weeks. In general, it is intended that you give 2 presentations. One at the beginning of your work and the second talk at about 2/3 of your thesis. In the first presentation, it is important to clarify the motivation and the research question, as well as the planned operationalisation. The expected time for this is 5 - 10 minutes. In the second presentation, the algorithms and procedures used should be explained and clarified to the audience. If possible, it is of course also welcome to present and discuss results that have already been achieved. The length of this presentation should be about 20-25 minutes. The final grade of the seminar is based on the two individual grades of the presentations.
- After each talk, there will be a short scientific discussion in which ALL listeners are invited to ask questions and make comments. With this in mind, it is appreciated that you are present for as many of the seminar dates as possible.
- The schedule of lectures will be displayed and updated on this website. Please check back regularly.
- Please enroll in the associated Moodle course. If necessary, I will contact you this way in the future.
- Seminars are live. The first session is for organizing the dates for your presentations. Please be there!
- Materials will be organized via this webiste.
- Email - important announcements will be sent out via mail (Using Moodle). Please check your associated email inbox regularly
RemarksReference Slides from previous semester
Scheduling appointments and organizational information
- Ahmad Dawar Hakimi: Contextualised Summarisation of Scholarly Documents (1)
- Joans Stahl: Adapting sentence embeddings to OCR erroneous data (1)
- Christian Staudte: Using Language Models for Generating Argumentation Knowledge (1)
- Fabian Frings: Applying a transformer-based machine learning technique to a Chatbot based Interactive Close Domain Question Answering System in Supportive Usage (1)
- Christian Staudte: Using Language Models for Generating Argumentation Knowledge (2)
- Karl Haase: The Impact of Near-Duplicates on Bootstrapping in Information Retrieval (1)
- Bernhard Jung: Early Hype Detection on Reddit (1)
- Moritz Brunsch: Multi-Label Active Learning with Many Irrelevant Examples (1)
- Fabian Frings: Applying a transformer-based machine learning technique to a Chatbot based Interactive Close Domain Question Answering System in Supportive Usage (2)
- Zhang Yaowei: Semi-automatic Knowledge Graph Authoring to Facilitate Retrieval of Expert Knowledge (1)
- Dominik Schwabe: Unsupervised Frame Identification in Argumentative Discussions (1)
- Mathias Halbauer: Analyse der Reaktionen von Coronamaßnahmen auf Basis von Twitterdaten (2)
- HiWi Vorträge
- Henrik Bininda: Crawling and Analyzing the Novelupdates Corpus (2)
- Charly Zimmer: Improving Causal Relation Extraction from the Web (1)
- Hannes Hansen: From contextual to static word embeddings (2)
- HiWi Vorträge
- Wolfgang Kircheis: Analyzing the History Section of Wikipedia Articles (2)
- Charly Zimmer: Improving Causal Relation Extraction from the Web (2)
- Jonas Stahl: Adapting sentence embeddings to OCR erroneous data (2)
- Zhang Yaowei: Semi-automatic Knowledge Graph Authoring to Facilitate Retrieval of Expert Knowledge (2)