We investigate automated methods to generate tagclouds for Computer Science
researchers based on keyphrase extraction methods and learning-to-rank models.
Given as input the identifier of an author in a bibliographical database
(currently DBLP), the method extracts links to the PDFs containing the
full-text of the paper. Keyphrase extraction methods are then applied to
extract multi-term tags from the text. In order to select the most important
tags for the researcher, we propose a set of features that serve as input for a
variety of learning-to-rank models. Evaluation is conducted with respect to 12
Computer Science professors, who score a selection of keyphrases extracted from
their papers indicating their relevance as a description of research topics.
These scores are used to train and compare various learning-to-rank models for
reordering the most important keyphrases, which in turn are used to generate
final tag clouds for the professors. We further validate the proposed
approaches by asking professors to evaluate the final tag-clouds.