Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents

Understand

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.