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Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces

Research article (Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2015) · cited 28× · AI/ML
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Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces

Summary

Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces is a scholarly article[1].

Key Facts

  • Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces's instance of is recorded as scholarly article[2].

Related Entities

Isabel Trancoso

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces. Retrieved May 24, 2026, from https://4ort.xyz/entity/learning-word-representations-from-scarce-and-noisy-data-with-embedding-subspaces
MLA “Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/learning-word-representations-from-scarce-and-noisy-data-with-embedding-subspaces.
BibTeX @misc{4ortxyz_learning-word-representations-from-scarce-and-noisy-data-with-embedding-subspaces_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces}}, year = {2026}, url = {https://4ort.xyz/entity/learning-word-representations-from-scarce-and-noisy-data-with-embedding-subspaces}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces — https://4ort.xyz/entity/learning-word-representations-from-scarce-and-noisy-data-with-embedding-subspaces (retrieved 2026-05-24)

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