Deep Sentence Embedding Using Long Short-Term Memory Networks

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文件名称：Deep Sentence Embedding Using Long Short-Term Memory Networks
文件大小：1.65MB
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更新时间：2020-03-22 04:38:09
深度学习 人工智能 自然语言处理 This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence.