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.

References in zbMATH (referenced in 87 articles )

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  1. Berk Wheelock, Lauren; Pachamanova, Dessislava A.: Acceptable set topic modeling (2022)
  2. Gfrerer, Helmut; Outrata, Jiří V.; Valdman, Jan: On the solution of contact problems with Tresca friction by the semismooth* Newton method (2022)
  3. Hettiarachchi, Hansi; Adedoyin-Olowe, Mariam; Bhogal, Jagdev; Gaber, Mohamed Medhat: Embed2detect: temporally clustered embedded words for event detection in social media (2022)
  4. Liu, Ruibo; Jia, Chenyan; Wei, Jason; Xu, Guangxuan; Vosoughi, Soroush: Quantifying and alleviating political bias in language models (2022)
  5. Loureiro, Daniel; Mário Jorge, Alípio; Camacho-Collados, Jose: LMMS reloaded: transformer-based sense embeddings for disambiguation and beyond (2022)
  6. Lu, Yaojie; Lin, Hongyu; Tang, Jialong; Han, Xianpei; Sun, Le: End-to-end neural event coreference resolution (2022)
  7. Ribeiro, Eugénio; Ribeiro, Ricardo; Martins de Matos, David: Automatic recognition of the general-purpose communicative functions defined by the ISO 24617-2 standard for dialog act annotation (2022)
  8. Albert Weichselbraun: Inscriptis - A Python-based HTML to text conversion library optimized for knowledge extraction from the Web (2021) arXiv
  9. Benjamin Paaßen, Jessica McBroom, Bryn Jeffries, Irena Koprinska, Kalina Yacef: ast2vec: Utilizing Recursive Neural Encodings of Python Programs (2021) arXiv
  10. Ghukasyan, Tsolak; Yeshilbashyan, Yeva; Avetisyan, Karen: Subwords-only alternatives to fastText for morphologically rich languages (2021)
  11. Grosnit, Antoine; Cowen-Rivers, Alexander I.; Tutunov, Rasul; Griffiths, Ryan-Rhys; Wang, Jun; Bou-Ammar, Haitham: Are we forgetting about compositional optimisers in Bayesian optimisation? (2021)
  12. Kadıoğlu, Serdar; Kleynhans, Bernard; Wang, Xin: Optimized item selection to boost exploration for recommender systems (2021)
  13. Koyyalagunta, Divya; Sun, Anna; Draelos, Rachel Lea; Rudin, Cynthia: Playing codenames with language graphs and word embeddings (2021)
  14. Lim, Suryani; Prade, Henri; Richard, Gilles: Classifying and completing word analogies by machine learning (2021)
  15. McPheat, Lachlan; Sadrzadeh, Mehrnoosh; Wazni, Hadi; Wijnholds, Gijs: Categorical vector space semantics for Lambek calculus with a relevant modality (extended abstract) (2021)
  16. Mingxiang Chen, Zhanguo Chang, Haonan Lu, Bitao Yang, Zhuang Li, Liufang Guo, Zhecheng Wang: AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation (2021) arXiv
  17. Moreo, Alejandro; Esuli, Andrea; Sebastiani, Fabrizio: Word-class embeddings for multiclass text classification (2021)
  18. Pakzad, Atefe; Analoui, Morteza: A word selection method for producing interpretable distributional semantic word vectors (2021)
  19. Prade, Henri; Richard, Gilles: Multiple analogical proportions (2021)
  20. Roman, Ibai; Santana, Roberto; Mendiburu, Alexander; Lozano, Jose A.: Evolution of Gaussian process kernels for machine translation post-editing effort estimation (2021)

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