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.
If you use the code, please kindly cite the following paper:
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. The 29th AAAI Conference on Artificial Intelligence (AAAI'15).
S. Wang, J. Tang, C. Aggarwal, и H. Liu. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, ACM, (октября 2016)
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)
G. Marco Baroni, Georgiana Dinu. 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference, (2014)
W. Zou, R. Socher, D. Cer, и C. Manning. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, стр. 1393--1398. (2013)
D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, и B. Qin. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), стр. 1555--1565. Baltimore, Maryland, Association for Computational Linguistics, (июня 2014)