Why bert instead of glove or word2vec?
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BERT is preferred over GloVe or Word2Vec because it has stronger semantic representation capabilities and can handle polysemous words. BERT is a bidirectional model that captures the context of words by considering both the left and right context, whereas GloVe and Word2Vec are based on the distributional hypothesis and only consider the surrounding words. BERT's contextual embeddings allow it to capture more nuanced meanings and improve the performance of sentiment classification models. Additionally, BERT has been shown to outperform GloVe and Word2Vec in various tasks, such as natural language inference and aspect sentiment classification. Therefore, researchers have chosen to use BERT as a text vectorization tool in sentiment analysis studies.
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| Papers (4) | Insight |
|---|---|
| The provided paper does not mention why BERT is used instead of GloVe or Word2Vec. | |
01 Jul 2022 1 Citations | The paper states that BERT is used instead of word2vec because BERT has stronger semantic representation capabilities and can handle polysemous words. |
01 Jul 2022 1 Citations | The paper states that BERT is used instead of word2vec because BERT has stronger semantic representation capabilities and can handle polysemous words. |
09 Jun 2021 | The paper does not explicitly mention why BERT is used instead of Glove or Word2Vec. |
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