Effectively making sense of short texts is a critical task for many real
world applications such as search engines, social media services, and
recommender systems. The task is particularly challenging as a short text
contains very sparse information, often too sparse for a machine learning
algorithm to pick up useful signals. A common practice for analyzing short text
is to first expand it with external information, which is usually harvested
from a large collection of longer texts. In literature, short text expansion
has been done with all kinds of heuristics. We propose an end-to-end solution
that automatically learns how to expand short text to optimize a given learning
task. A novel deep memory network is proposed to automatically find relevant
information from a collection of longer documents and reformulate the short
text through a gating mechanism. Using short text classification as a
demonstrating task, we show that the deep memory network significantly
outperforms classical text expansion methods with comprehensive experiments on
real world data sets.

本文提出了一种全新的深度记忆网络来帮助短文本扩展，从而提高短文本分类等机器学习任务的效果。实验结果表明，该方法在真实数据集上显著优于传统的文本扩展方法。

短文本扩展的端到端学习

End-to-end Learning for Short Text Expansion

We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exact sentiment towards an identified entity (the target) into positive,
negative, or neutral.
Instead, we show that attitude identification can be solved with an
end-to-end machine learning architecture, in which the two subtasks are
interleaved by a deep memory network. In this way, signals produced in target
detection provide clues for polarity classification, and reversely, the
predicted polarity provides feedback to the identification of targets.
Moreover, the treatments for the set of targets also influence each other --
the learned representations may share the same semantics for some targets but
vary for others. The proposed deep memory network, the AttNet, outperforms
methods that do not consider the interactions between the subtasks or those
among the targets, including conventional machine learning methods and the
state-of-the-art deep learning models.

本文提出了一种利用端到端机器学习架构解决主体、情感分类、以及二者交互的深度记忆网络模型，其表现优于传统机器学习方法和当前最先进的深度学习模型。

深度记忆网络用于态度识别

Deep Memory Networks for Attitude Identification

We introduce a deep memory network for aspect level sentiment classification.
Unlike feature-based SVM and sequential neural models such as LSTM, this
approach explicitly captures the importance of each context word when inferring
the sentiment polarity of an aspect. Such importance degree and text
representation are calculated with multiple computational layers, each of which
is a neural attention model over an external memory. Experiments on laptop and
restaurant datasets demonstrate that our approach performs comparable to
state-of-art feature based SVM system, and substantially better than LSTM and
attention-based LSTM architectures. On both datasets we show that multiple
computational layers could improve the performance. Moreover, our approach is
also fast. The deep memory network with 9 layers is 15 times faster than LSTM
with a CPU implementation.

本文提出了基于深度记忆网络的方面级情感分类模型，并通过多层神经注意力模型计算上下文词汇的重要程度和文本表示，在笔记本和餐厅数据集上的实验结果表明，这种方法的表现与现有的 SVM 系统相媲美，在多个指标方面优于 LSTM 和基于注意力的 LSTM 结构，运行速度也非常快。