Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

Claudia Vlad

dblp:425/9127 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Information extraction and text analysis · 87% Machine translation · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › multilingual NLP
cognate identification		 0.912025
Friend or Foe? A Computational Investigation of Semantic False Friends across Romance Languages · EMNLP 2025
Natural language and speech › Information extraction and text analysis
lexical semantics		 0.912025
Friend or Foe? A Computational Investigation of Semantic False Friends across Romance Languages · EMNLP 2025

Methods — techniques the papers use, named apart from their topics

etymological dictionary analysis · 0.9
YearPublicationVenuePosition
2025 Friend or Foe? A Computational Investigation of Semantic False Friends across Romance Languages
abstract
In this paper we present a comprehensive analysis of lexical semantic divergence between cognate words and borrowings in the Romance languages.We experiment with different algorithms for false friend detection including deceptive cognate and deceptive borrowings and correction and evaluate them systematically on cognate and borrowing pairs in the five Romance languages.We use the most complete and reliable dataset of cognate words and borrowings based on etymological dictionaries for the five main Romance languages (Italian, Spanish, Portuguese, French and Romanian) to extract deceptive cognates and borrowings automatically based on usage, and freely publish the lexicon of obtained true and deceptive cognate and borrowings in every Romance language pair.
Ana Sabina Uban, Liviu P. Dinu, Ioan-Bogdan Iordache, Simona Georgescu, Claudia Vlad
 EMNLP5