J
Jérémy Ferrero
Researcher at University of Grenoble
Publications - 15
Citations - 177
Jérémy Ferrero is an academic researcher from University of Grenoble. The author has contributed to research in topics: Plagiarism detection & Sentence. The author has an hindex of 7, co-authored 15 publications receiving 146 citations.
Papers
More filters
Proceedings ArticleDOI
Using Word Embedding for Cross-Language Plagiarism Detection
TL;DR: This paper introduces new cross-language similarity detection methods based on distributed representation of words and combines the different methods proposed to verify their complementarity, obtaining an overall F1 score of 89.15% for English-French similarity detection at chunk level.
Proceedings Article
A Multilingual, Multi-Style and Multi-Granularity Dataset for Cross-Language Textual Similarity Detection
TL;DR: The proposed dataset is multilingual, includes cross-language alignment for different granularities (from chunk to document), is based on both parallel and comparable corpora and contains human and machine translated texts.
Book ChapterDOI
Word Embedding-Based Approaches for Measuring Semantic Similarity of Arabic-English Sentences
TL;DR: Two word embedding-based approaches devoted to measuring the semantic similarity between Arabic-English cross-language sentences are proposed and the proposed methods are confirmed through the Pearson correlation between the similarity scores and human ratings.
Proceedings ArticleDOI
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting
TL;DR: LIG proposes an innovative enhancement to word embedding-based model devoted to measure the semantic similarity in Arabic sentences to exploit the word representations as vectors in a multidimensional space to capture the semantic and syntactic properties of words.
Posted Content
UsingWord Embedding for Cross-Language Plagiarism Detection
TL;DR: This paper proposed to use distributed representation of words (word embeddings) in cross-language textual similarity detection and obtained an overall F1 score of 89.15% for English-French similarity detection at chunk level (88.5% at sentence level) on a very challenging corpus.