Summary of Learning Word Embedding with Better Distance Weighting and Window Size Scheduling, by Chaohao Yang and Chris Ding
Learning Word Embedding with Better Distance Weighting and Window Size Scheduling
by Chaohao Yang, Chris Ding
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper enhances the popular Word2Vec word embedding model by incorporating distance information between center and context words. Two novel methods, Learnable Formulated Weights (LFW) and Epoch-based Dynamic Window Size (EDWS), are introduced to improve the performance of two variants of Word2Vec: Continuous Bag-of-Words (CBOW) and Continuous Skip-gram (Skip-gram). LFW uses learnable parameters to calculate distance-related weights for average pooling, providing insights for future NLP text modeling research. EDWS improves the dynamic window size strategy in Skip-gram by introducing distance information in a more balanced way. Experimental results show that LFW and EDWS outperform previous state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes Word2Vec better by adding distance information between words. This helps the model understand how words are related to each other. The authors introduce two new ways to do this: Learnable Formulated Weights (LFW) and Epoch-based Dynamic Window Size (EDWS). LFW uses special weights that learn how to combine words based on their distance. EDWS makes Skip-gram work better by introducing distance information in a balanced way. The results show that these new methods make Word2Vec perform even better. |
Keywords
» Artificial intelligence » Bag of words » Embedding » Nlp » Word2vec
