10 citations · 29 across the 9 of their papers we have counts for
9 papers
TAPR: Enhancing LLM Performance with a Task-Aware Prompt Rewriter
Oliver Savolainen, Emanuele Bastianelli, Hosein Azarbonyad
Large Language Models (LLMs) often require carefully crafted prompts to unlock their full potential, which can be a barrier for non-expert users. This work addresses the challenge…
Question-Answer Extraction from Scientific Articles Using Knowledge Graphs and Large Language Models
Hosein Azarbonyad, Zi Long Zhu, Georgios Cheirmpos +3
When deciding to read an article or incorporate it into their research, scholars often seek to quickly identify and understand its main ideas. In this paper, we aim to extract thes…
Generating Topic Pages for Scientific Concepts Using Scientific Publications
Hosein Azarbonyad, Zubair Afzal, George Tsatsaronis
In this paper, we describe Topic Pages, an inventory of scientific concepts and information around them extracted from a large collection of scientific books and journals. The main…
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…
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…
Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps +1
Deep neural networks have become a primary tool for solving problems in many fields. They are also used for addressing information retrieval problems and show strong performance in…
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity
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…
On Horizontal and Vertical Separation in Hierarchical Text Classification
Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps +1
Hierarchy is a common and effective way of organizing data and representing their relationships at different levels of abstraction. However, hierarchical data dependencies cause di…
Generalized Group Profiling for Content Customization
Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps +1
There is an ongoing debate on personalization, adapting results to the unique user exploiting a user's personal history, versus customization, adapting results to a group profile s…