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.

Note! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20 for the autumn semester/January 20 for the spring semester. 

• 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.