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 take
•All $GPxxx sentence codes and short descr iptions •26 interpreted sentences transmitted by GPS unit •12 interpreted Garmin proprietary sentences transmitted by GPS unit •8 interpreted Garmin proprietary sentences received by GPS unit •Format of lat
In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only sm
用最简单的模型、最简单的特征工程做出好效果，追求的就是极致性价比。如果有需要，可以在此基础上做一些模型更改和特征工程，提高表现效果。ture for face verification developed by Chopra, Hadsell, and This forces the LSTm to entirely capture the semantic dif-
LeCun(2005), which utilizes symmetric Conv Nets where ferences d
