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.
