38 citations · 85 across the 7 of their papers we have counts for
9 papers
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents
Hosein Azarbonyad, Mostafa Dehghani, Tom Kenter +3
A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies…
Learning to Rank from Samples of Variable Quality
Mostafa Dehghani, Jaap Kamps
Training deep neural networks requires many training samples, but in practice, training labels are expensive to obtain and may be of varying quality, as some may be from trusted ex…
Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision
Mostafa Dehghani, Aliaksei Severyn, Sascha Rothe +1
Training deep neural networks requires massive amounts of training data, but for many tasks only limited labeled data is available. This makes weak supervision attractive, using we…
Learning to Learn from Weak Supervision by Full Supervision
Mostafa Dehghani, Aliaksei Severyn, Sascha Rothe +1
In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model…
On Search Powered Navigation
Mostafa Dehghani, Glorianna Jagfeld, Hosein Azarbonyad +3
Query-based searching and browsing-based navigation are the two main components of exploratory search. Search lets users dig in deep by controlling their actions to focus on and fi…
Finding Talk About the Past in the Discourse of Non-Historians
Alex Olieman, Kaspar Beelen, Jaap Kamps
A heightened interest in the presence of the past has given rise to the new field of memory studies, but there is a lack of search and research tools to support studying how and wh…