Deconstructing Word Embedding Algorithms
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Summary
This work deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of the common conditions that seem to be required for making performant word embeddings.
- Type
- preprint
- Published
- 2020-11-01
- Cited by
- 8
- References
- 17
- Access
- Open access
- OpenAlex
- https://openalex.org/W3099354896
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:226262352
Keywords
Word2vec, Word (group theory), Computer science, Word embedding, Natural language processing
References
- word2vec Parameter Learning Explained
- Linking GloVe with word2vec
- Improving Distributional Similarity with Lessons Learned from Word Embeddings
- Frequency Estimates for Statistical Word Similarity Measures
- Neural Word Embedding as Implicit Matrix Factorization
- Distributed Representations of Words and Phrases and their Compositionality
- GloVe: Global Vectors for Word Representation
- A Unified Learning Framework of Skip-Grams and Global Vectors
- Swivel: Improving Embeddings by Noticing What's Missing
- Word Embedding Revisited: A New Representation Learning and Explicit Matrix Factorization Perspective
- Improving Document Ranking with Dual Word Embeddings
- Bag of Tricks for Efficient Text Classification
- A Latent Variable Model Approach to PMI-based Word Embeddings
- A Simple but Tough-to-Beat Baseline for Sentence Embeddings
- Generalized Low Rank Models
- Generalized Low Rank Models
Cited by
- To Know by the Company Words Keep and What Else Lies in the Vicinity
- ALANNO: An Active Learning Annotation System for Mortals
- EigenNoise: A Contrastive Prior to Warm-Start Representations
- Dataset for identification of queerphobia
- Contrastive Learning as Kernel Approximation
- Optimizing Academic Pairings in Smart Campuses: A Recommendation System for Academic Communities
- Is neural semantic parsing good at ellipsis resolution, or isn't it?
- Text Representations and Word Embeddings
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