# Learning Semantically and Additively Compositional Distributional Representations

arXiv: https://arxiv.org/abs/1606.02461 · HTML: https://mlsanity.com/paper/1606.02461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [A Systematic Review of Reproducibility Research in Natural Language Processing](https://mlsanity.com/paper/2103.07929/) (2021)

## Abstract (arXiv)

This paper connects a vector-based composition model to a formal semantics, the Dependency-based Compositional Semantics (DCS). We show theoretical evidence that the vector compositions in our model conform to the logic of DCS. Experimentally, we show that vector-based composition brings a strong ability to calculate similar phrases as similar vectors, achieving near state-of-the-art on a wide range of phrase similarity tasks and relation classification; meanwhile, DCS can guide building vectors for structured queries that can be directly executed. We evaluate this utility on sentence completion task and report a new state-of-the-art.