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Eric Gaussier
Researcher at University of Grenoble
Publications - 242
Citations - 10071
Eric Gaussier is an academic researcher from University of Grenoble. The author has contributed to research in topics: Language model & Divergence-from-randomness model. The author has an hindex of 41, co-authored 231 publications receiving 8203 citations. Previous affiliations of Eric Gaussier include French Institute for Research in Computer Science and Automation & Naver Corporation.
Papers
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Book ChapterDOI
A probabilistic interpretation of precision, recall and F -score, with implication for evaluation
Cyril Goutte,Eric Gaussier +1 more
TL;DR: A probabilistic setting is used which allows us to obtain posterior distributions on these performance indicators, rather than point estimates, and is applied to the case where different methods are run on different datasets from the same source.
Proceedings Article
Complex embeddings for simple link prediction
TL;DR: This work makes use of complex valued embeddings to solve the link prediction problem through latent factorization, and uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors.
Posted Content
Complex Embeddings for Simple Link Prediction
TL;DR: In this article, the authors make use of complex valued embeddings to handle a large variety of binary relations, among them symmetric and antisymmetric relations, and their approach is scalable to large datasets as it remains linear in both space and time.
Journal ArticleDOI
An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition
George Tsatsaronis,Georgios Balikas,Prodromos Malakasiotis,Ioannis Partalas,Matthias Zschunke,Michael R. Alvers,Dirk Weissenborn,Anastasia Krithara,Sergios Petridis,Dimitris Polychronopoulos,Yannis Almirantis,John Pavlopoulos,Nicolas Baskiotis,Patrick Gallinari,Thierry Artières,Axel-Cyrille Ngonga Ngomo,Norman Heino,Eric Gaussier,Liliana Barrio-Alvers,Michael Schroeder,Ion Androutsopoulos,Georgios Paliouras +21 more
TL;DR: Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.
Proceedings ArticleDOI
Relation between PLSA and NMF and implications
Eric Gaussier,Cyril Goutte +1 more
TL;DR: It is shown that PLSA solves the problem of NMF with KL divergence, and the implications of this relationship are explored.