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Unsupervised text classification word2vec

One of the most interesting algorithm that we used was Word2vec (actually word2vec is a set of algorithms).
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Oct 12, 2020 &183; Word2vec is an embedding technique that uses a 2 layers neural network model to learn word associations from a large corpus of text.

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The quest for learning language representations by pre-training models on large unlabelled text data started from word embeddings like Word2Vec and GloVe.

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bin and use it in the text classification model (CNN) as my words initialiser, if I could fine-tune the word-to-vector map in vec.
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One of the most interesting algorithm that we used was Word2vec (actually word2vec is a set of algorithms).

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Word2Vec detects the contextual similarity of words mathematically through its neural network.
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Using natural language processing (NLP), text classifiers can analyze and sort text by sentiment, topic, and customer intent faster and more accurately than humans.
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Therefore, the weighting scheme tf-idf is used along with Word2Vec and can also improve the classification accuracy.

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Word2vec is not a single algorithm but a combination of two techniques CBOW(Continuous bag of words) and Skip-gram model.
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This content is paid for by the advertiser and published by WP BrandStudio. The Washington Post newsroom was not involved in the creation of this content. youtube vanced icon
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The main approach is tied around representing the text in a meaningful way whether through TF-IDF, Word2Vec, or more advanced models like BERT and training models on the representations as labeled inputs.

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