In natural language understanding, there is a hierarchy of lenses through which we can extract meaning - from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics.
I made an introductory talk on word embeddings in the past and this write-up is an extended version of the part about philosophical ideas behind word vectors.
ConceptNet Numberbatch consists of state-of-the-art semantic vectors (also known as word embeddings) that can be used directly as a representation of word meanings or as a starting point for further machine learning.
L. Hettinger, A. Zehe, A. Dallmann, и A. Hotho. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, стр. 191-204. Bonn, Gesellschaft für Informatik e.V., (2019)
M. Artetxe, G. Labaka, I. Lopez-Gazpio, и E. Agirre. Proceedings of the 22nd Conference on Computational Natural Language Learning, стр. 282--291. Association for Computational Linguistics, (2018)