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Update README.rst
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‎README.rst‎

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Comparison of Feature Extraction Techniques
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| **Model** | **Advantages** | **Limitation** |
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| **GloVe (Trained)** | * It is very straightforward, e.g., to enforce the word vectors to capture sub-linear relationships in the vector space (performs better than Word2vec) | * Memory consumption for storage |
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| | * Lower weight for highly frequent word pairs, such as stop words like “am”, “is”, etc. Will not dominate training progress | * Needs huge corpus to learn |
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| | * Lower weight for highly frequent word pairs, such as stop words like “am”, “is”, etc. Will not dominate training progress | * Needs huge corpus to learn |
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| | | * It cannot capture out-of-vocabulary words from the corpus |
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Dimensionality Reduction
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