The course Information Retrieval (IR) consists of a theoretical and a practical part.
For the theoretical part, students can describe and explain techniques, methods, and models related to information retrieval (searching for information). They can relate models to each other, identify differences and similarities, apply the models in practice, and modify and create models to suit other tasks. Finally, they understand how to evaluate IR systems.
On the practical side, students are able to perform IR experiments in which they transform theoretical models to a working system. This system is tested on a predefined task and dataset, after which students apply evaluation methodologies and examine and analyze the results to draw conclusions about the applied models.
The underlying question behind this course is: How do search engines work? To answer this question we dive into the details of information retrieval, the field that deals with search. During the course we discuss the various parts of search engines:
- Evaluation: given a working retrieval system, how do we determine its performance and how can we compare it to other systems?
- Relevance models: how do we retrieve relevant documents for a given query? And how do we rank these documents in the right order?
- Combining evidence: how do we combine evidence of a user perceived relevance of a document to a query (e.g. query-document similarity, authors authority, user’s clicks, etc) to rank documents?
- Applications: what are some of the important applications of information retrieval systems? How are information retrieval systems situated within a broader information seeking environment?
During the course the students are required to perform IR experiments. These experiments follow a setup similar to well-known evaluation campaigns (e.g., TREC) and use their data. Goal of these experiments is to get acquainted with IR experimental methodology, get hands on experience with open source retrieval systems and large datasets, and to be able to apply and adjust theoretical models to fit the task at hand. Besides running the experiments, evaluating and analyzing the results in an important part of the practical side of this course
RECOMMENDED PRIOR KNOWLEDGE
Basic machine learning. Basic python. Basic natural language processing.