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    [CS 297 Proposal]

    [Comparison between hash2vec and word2vec -pdf]

    [Different Approaches for word2vec from reference paper -pdf]

    [Deliverable 1]

    [Deliverable 2]

    [Deliverable 3]

    [Deliverable 4]

    [Deliverable 5]


    [CS 298 Proposal]



My first deliverable is an example program of wordtovec implemented in TensorFlow and also using gensim. This program used softmax to convert words into vector. Prior to implementation, I studied machine learning, neural network and Python. Word embedding is a parameterized function mapping words in some language to high-dimensional vectors. Methods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, and explicit representation in terms of the context in which words appear. A word embedding is sometimes called a word representation or a word vector. It maps words to a high dimensional vector of real numbers. The meaningful vector learned can be used to perform some task.