  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.
