Comparison between cbow, skip gram and skip-gram with subword information
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A practical comparison between the different Word2Vec architectures. These are companion slides for this article: https://kavita-ganesan.com/comparison-between-cbow-skipgram-subword/
![Comparison Between CBOW, Skip-
Gram and Skip-Gram with Subword
Information [ See article ]
By: Kavita Ganesan](https://image.slidesharecdn.com/comparisonbetweencbowskip-gramandskip-gramwithsubwordinformation-200424150406/85/comparison-between-cbow-skip-gram-and-skipgram-with-subword-information-1-320.jpg)
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Comparison between cbow, skip gram and skip-gram with subword information
- 1. Comparison Between CBOW, Skip- Gram and Skip-Gram with Subword Information [ See article ] By: Kavita Ganesan
- 2. CBOW This is a visual comparison sum visual thisis acomparison visual thisis acomparison visual visual visual target word By: Kavita Ganesan target word SkipGram
- 3. This is a visual comparison sum visual th ishi target word SkipGram + Subword (SkipGramSI) this<t s>
- 4. Training Data & Model Settings
- 6. Differences in Training time With Gensim
- 7. Task 1: Most related concepts
- 12. Task 2: Similarity between words
- 14. Task 3: Similarity between phrases
- 15. Dataset
- 16. Precision, Recall & F-Score Cosine threshold = 0.6
- 17. Raw Predictions
- 18. Final Thoughts Plain word embeddings: ● Does not capture fine-grained semantic similarity ● Words are not directly replaceable with words closest in vector space ● Quality of embeddings is only as good as the training data