Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context words when building a learning system. I...

Long Short-Term Memory (LSTM) neural networks are recurrent neural networks that contain memory units that can store contextual information from past inputs for arbitrary amounts of time. A typical LSTM neural network language model is trained by feeding an input sequence. i.e., a stream of words, to the input layer of the network and the output layer predicts the probability of the next word g...

This paper presents a dialogue response generator based on long short term memory (LSTM) neural networks for the SLG (Spoken Language Generation) pilot task of DSTC5 [1]. We first encode the input containing different number of semantic units as fixed-length semantic vector with a LSTM encoder. Then we decode the semantic vector with a variant of LSTM and generate corresponding text. In order t...

We propose a simple mathematical definition and new neural architecture for finding anomalies within discrete sequence datasets. Our model comprises of a modified LSTM autoencoder and an array of One-Class SVMs. The LSTM takes in elements from a sequence and creates context vectors that are used to predict the probability distribution of the following element. These context vectors are then use...

Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform traditional RNNs when dealing with sequences involving not only short-term but also long-term dependencies. The decoupled extended Kalman filter learning algorithm (DEKF) works well in online environments and reduces significantly the number of training steps when compared to the standard gradient-descent algorithms. Prev...

We introduce multiplicative LSTM (mLSTM), a novel recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible input, which we argue makes it more expressive for autoregressive density ...

Unlike traditional recurrent neural networks, the Long ShortTerm Memory (LSTM) model generalizes well when presented with training sequences derived from regular and also simple nonregular languages. Our novel combination of LSTM and the decoupled extended Kalman filter, however, learns even faster and generalizes even better, requiring only the 10 shortest exemplars (n ≤ 10) of the context sen...

This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks (LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results ...