On the Various Semantics of Similarity in Word Embedding Models. - Ábel Elekes; Martin Schäler; Klemens Böhm (JCDL, 2017)

Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data. - Ábel Elekes; Adrian Englhardt; Martin Schäler; Klemens Böhm (CoNLL, 2018)

Learning from Few Samples: Lexical Substitution with Word Embeddings for Short Text Classification. - Ábel Elekes; Antonino Simone Di Stefano; Martin Schäler; Klemens Böhm; Matthias Keller (JCDL, 2019)

Toward meaningful notions of similarity in NLP embedding models. - Ábel Elekes; Adrian Englhardt; Martin Schäler; Klemens Böhm (Int. J. Digit. Libr., 2020)

Visit Places on Your Way: A Skyline Approach in Time-Dependent Networks. - Saeed Taghizadeh; Ábel Elekes; Martin Schäler (MDM, 2021)

How meaningful are similarities in deep trajectory representations? - Saeed Taghizadeh; Ábel Elekes; Martin Schäler; Klemens Böhm (Inf. Syst., 2021)

Three-Way Analysis for a Better Understanding of Word Embedding Models. - Ábel Elekes (2021)