Information Retrieval and Text Mining (DAT640)

The course offers an introduction to techniques and methods for processing, mining, and searching in massive text collections. The course considers a broad variety of applications and provides an opportunity for hands-on experimentation with state-of-the-art algorithms using existing software tools and data collections.

NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20th.

• Text preprocessing, indexing
• Representation learning (word embeddings)
• Text categorization
• Search engine architecture
• Retrieval models (vector-space model, probabilistic models, learning to rank, neural models)
• Search engine evaluation
• Query modeling, relevance feedback
• Web search (link analysis)
• Semantic search (knowledge bases, entity retrieval, entity linking)
• Conversational information access
• Transformers and large language models

Knowledge:

• Theory and practice of concepts, methods, and techniques for managing and analyzing large amounts of text data.

Skills:

• Process and prepare large-scale textual data collections for retrieval and mining.
• Apply retrieval, classification, and clustering methods to a range of information access problems.
• Conduct performance evaluation and error analysis.

General competencies:

• Understanding of the strengths and limitations of modern information retrieval and text mining techniques. Being able to identify promising business applications, participate in and lead such projects.