# Dependency-based Convolutional Neural Networks for Sentence Embedding

arXiv: https://arxiv.org/abs/1507.01839 · HTML: https://mlsanity.com/paper/1507.01839/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2015.

## Built on by (most cited first, 3 of 3)

- [Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models](https://mlsanity.com/paper/2106.08689/) (2021)
- [Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task](https://mlsanity.com/paper/2201.07112/) (2022)
- [Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset](https://mlsanity.com/paper/2308.13545/) (2023)

## Abstract (arXiv)

In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning and linguistic structures, we propose a tree-based convolutional neural network model which exploit various long-distance relationships between words. Our model improves the sequential baselines on all three sentiment and question classification tasks, and achieves the highest published accuracy on TREC.