Adrian Popescu


Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations
Evan Dufraisse | Célina Treuillier | Armelle Brun | Julien Tourille | Sylvain Castagnos | Adrian Popescu
Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences

Online news consumption plays an important role in shaping the political opinions of citizens. The news is often served by recommendation algorithms, which adapt content to users’ preferences. Such algorithms can lead to political polarization as the societal effects of the recommended content and recommendation design are disregarded. We posit that biases appear, at least in part, due to a weak entanglement between natural language processing and recommender systems, both processes yet at work in the diffusion and personalization of online information. We assume that both diversity and acceptability of recommended content would benefit from such a synergy. We discuss the limitations of current approaches as well as promising leads of opinion-mining integration for the political news recommendation process.


Building Specialized Bilingual Lexicons Using Large Scale Background Knowledge
Dhouha Bouamor | Adrian Popescu | Nasredine Semmar | Pierre Zweigenbaum
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

LIPN-CORE: Semantic Text Similarity using n-grams, WordNet, Syntactic Analysis, ESA and Information Retrieval based Features
Davide Buscaldi | Joseph Le Roux | Jorge J. García Flores | Adrian Popescu
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity


A Conceptual Approach to Web Image Retrieval
Adrian Popescu | Gregory Grefenstette
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

People use the Internet to find a wide variety of images. Existing image search engines do not understand the pictures they return. The introduction of semantic layers in information retrieval frameworks may enhance the quality of the results compared to existing systems. One important challenge in the field is to develop architectures that fit the requirements of real-life applications, like the Internet search engines. In this paper, we describe Olive, an image retrieval application that exploits a large scale conceptual hierarchy (extracted from WordNet) to automatically reformulate user queries, search for associated images and present results in an interactive and structured fashion. When searching a concept in the hierarchy, Olive reformulates the query using its deepest subtypes in WordNet. On the answers page, the system displays a selection of related classes and proposes a content based retrieval functionality among the pictures sharing the same linguistic label. In order to validate our approach, we run to series of tests to assess the performances of the application and report the results here. First, two precision evaluations over a panel of concepts from different domains are realized and second, a user test is designed so as to assess the interaction with the system.