GloVe

GloVe: Global Vectors for Word Representation. GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

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References in zbMATH (referenced in 46 articles )

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  1. Aggarwal, Charu C.: Linear algebra and optimization for machine learning. A textbook (2020)
  2. Bassu, Devasis; Jones, Peter W.; Ness, Linda; Shallcross, David: Product formalisms for measures on spaces with binary tree structures: representation, visualization, and multiscale noise (2020)
  3. Dillon Niederhut: niacin: A Python package for text data enrichment (2020) not zbMATH
  4. Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, Tarek Abdelzaher: paper2repo: GitHub Repository Recommendation for Academic Papers (2020) arXiv
  5. Jisung Yoon, Kai-Cheng Yang, Woo-Sung Jung, Yong-Yeol Ahn: Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs (2020) arXiv
  6. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  7. Lee, Gee Y.; Manski, Scott; Maiti, Tapabrata: Actuarial applications of word embedding models (2020)
  8. Nguyen, Thi Thanh Sang; Do, Pham Minh Thu: Classification optimization for training a large dataset with naïve Bayes (2020)
  9. Ruiz, Francisco J. R.; Athey, Susan; Blei, David M.: SHOPPER: a probabilistic model of consumer choice with substitutes and complements (2020)
  10. Subhabrata Mukherjee, Ahmed Awadallah: TinyMBERT: Multi-Stage Distillation Framework for Massive Multi-lingual NER (2020) arXiv
  11. Vaibhav Kumar, Tenzin Singhay Bhotia, Vaibhav Kumar: Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings (2020) arXiv
  12. Valaitis, Vytautas; Marcinkevicius, Virginijus; Jurevicius, Rokas: Learning aerial image similarity using triplet networks (2020)
  13. Yada Pruksachatkun, Phil Yeres, Haokun Liu, Jason Phang, Phu Mon Htut, Alex Wang, Ian Tenney, Samuel R. Bowman: jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models (2020) arXiv
  14. Yang, Puyudi; Chen, Jianbo; Hsieh, Cho-Jui; Wang, Jane-Ling; Jordan, Michael I.: Greedy attack and Gumbel attack: generating adversarial examples for discrete data (2020)
  15. Cichosz, Paweł: A case study in text mining of discussion forum posts: classification with bag of words and global vectors (2019)
  16. Da, Qiaobo; Cheng, Jieren; Li, Qian; Zhao, Wentao: Socially-attentive representation learning for cold-start fraud review detection (2019)
  17. Furbach, Ulrich; Krämer, Teresa; Schon, Claudia: Names are not just sound and smoke: word embeddings for axiom selection (2019)
  18. Ghatak, Abhijit: Deep learning with R (2019)
  19. Jibril Frej, Didier Schwab, Jean-Pierre Chevallet: WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset (2019) arXiv
  20. Lüddecke, Timo; Agostini, Alejandro; Fauth, Michael; Tamosiunaite, Minija; Wörgötter, Florentin: Distributional semantics of objects in visual scenes in comparison to text (2019)

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