# Distributional semantics beyond words: Supervised learning of analogy and paraphrase

arXiv: https://arxiv.org/abs/1310.5042 · HTML: https://mlsanity.com/paper/1310.5042/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2013.

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

- [Distilling Relation Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2110.15705/) (2021)
- [Probabilistic Analogical Mapping with Semantic Relation Networks](https://mlsanity.com/paper/2103.16704/) (2021)
- [The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories](https://mlsanity.com/paper/2501.12651/) (2025)

## Abstract (arXiv)

There have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (SAT analogies and SemEval~2012 Task 2) and measuring compositional similarity between noun-modifier phrases and unigrams (multiple-choice paraphrase questions).