The goal of sentence and document modeling is to accurately represent the
meaning of sentences and documents for various Natural Language Processing
tasks. In this work, we present Dependency Sensitive Convolutional Neural
Networks (DSCNN) as a general-purpose classification system for both sentences
and documents. DSCNN hierarchically builds textual representations by
processing pretrained word embeddings via Long Short-Term Memory networks and
subsequently extracting features with convolution operators. Compared with
existing recursive neural models with tree structures, DSCNN does not rely on
parsers and expensive phrase labeling, and thus is not restricted to
sentence-level tasks. Moreover, unlike other CNN-based models that analyze
sentences locally by sliding windows, our system captures both the dependency
information within each sentence and relationships across sentences in the same
document. Experiment results demonstrate that our approach is achieving
state-of-the-art performance on several tasks, including sentiment analysis,
question type classification, and subjectivity classification.

本研究旨在通过 Dependency Sensitive Convolutional Neural Networks (DSCNN) 分类系统实现句子和文档的表示，不需要依靠解析器和昂贵的短语标记，拓展了句子层级处理的任务范围，并以其在情感分析、问题分类和主观分类等任务中取得的最新表现获得了验证。

依赖敏感卷积神经网络用于建模句子和文档

Dependency Sensitive Convolutional Neural Networks for Modeling  Sentences and Documents

The ability to accurately model a sentence at varying stages (e.g.,
word-phrase-sentence) plays a central role in natural language processing. As
an effort towards this goal we propose a self-adaptive hierarchical sentence
model (AdaSent). AdaSent effectively forms a hierarchy of representations from
words to phrases and then to sentences through recursive gated local
composition of adjacent segments. We design a competitive mechanism (through
gating networks) to allow the representations of the same sentence to be
engaged in a particular learning task (e.g., classification), therefore
effectively mitigating the gradient vanishing problem persistent in other
recursive models. Both qualitative and quantitative analysis shows that AdaSent
can automatically form and select the representations suitable for the task at
hand during training, yielding superior classification performance over
competitor models on 5 benchmark data sets.

通过提出自适应层次句子模型 (AdaSent)，解决其他递归模型中的梯度消失问题，以及利用竞争机制在分类任务中自动选择适合任务的表示形式，使 AdaSent 在 5 个基准数据集上表现出优异的表现。

自适应分层句子模型

Self-Adaptive Hierarchical Sentence Model

This paper proposes a tree-based convolutional neural network (TBCNN) for
discriminative sentence modeling. Our models leverage either constituency trees
or dependency trees of sentences. The tree-based convolution process extracts
sentences' structural features, and these features are aggregated by max
pooling. Such architecture allows short propagation paths between the output
layer and underlying feature detectors, which enables effective structural
feature learning and extraction. We evaluate our models on two tasks: sentiment
analysis and question classification. In both experiments, TBCNN outperforms
previous state-of-the-art results, including existing neural networks and
dedicated feature/rule engineering. We also make efforts to visualize the
tree-based convolution process, shedding light on how our models work.

本研究提出了一种基于树的卷积神经网络（TBCNN），用于区分性句子建模。文章通过 constituency trees 或 dependency trees 提取句子的结构特征，并通过最大池聚合这些特征。我们评估了我们的模型在情感分析和问题分类两个领域的表现，TBCNN 的表现均优于以前的最新结果，包括现有的神经网络和专门的特征 / 规则工程方法。我们还努力可视化基于树的卷积过程，来解释我们的模型工作原理。