Data Analysis Software Project for Natural Language

Data Analysis Software Project for Natural Language

TUCaN: 20-00-0948-pp / Moodle: DASP19/20
The course will be taught in English. If you have any questions regarding this course, please contact Lucie Flekova.

Dates

  • 17.10.2019, 09:50-11:30 (S101/A3)
    We will present the project ideas and the requirements for this course
  • 22.10.2019
    Deadline for project selection. The students inform us about their preferred topics and groups
  • 24.10.2019, 09:50-11:30 (S202/A126)
    We will discuss the details for each project, clarify tasks, and confirm or change the topic assignments
  • 23.01.2020 / 30.01.2020 / 06.02.2020, 9:50-11:30
    The students will present their project results
  • 13.02.2020
    Project hand-in. This is the end of this course.

Course Content

In this software project, students will address topics related to natural language processing of conversational data resulting in chatbots and their new skills. For example, they will develop software to mine social networks and the web, selecting useful information from the acquired raw data (e.g. matching a topic, finding arguments or answering questions), improving a chatbot prototype, matching topic/posts to websites or documents, and addressing ethical issues with open conversational content. Possible project topics will be discussed in the first lecture.

The students' task in this course will be to execute one of the projects in a small group or individually. The mentoring research staff will define the project requirements. Students will regularly meet with their mentor to discuss the progress of the project. The best projects related to mining scientific conversations and content will be made available to the NLP research community.

Topics

We will address topics related to natural language processing of conversational data:

  • Feeding a conversational agent for NLP topics
  • Argumentation in author-reviewer paper discussions
  • Opinion change on controversial topics in social media
  • Expanding scientific paper repositories with user discussions from social media

Format

Students will work on the chosen projects in small groups. Students will regularly meet with their mentor to discuss the progress of the project, and they are required to hand-in short status reports to the teaching staff.

Requirements

  • Interest in working with natural language textual data
  • Programming skills (most projects are going to use Python)

Teaching Staff


Regular office hours: Thursday 09:50-11:30 (please contact by e-mail for an appointment)

Prof. Dr. Iryna Gurevych