# Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations

arXiv: https://arxiv.org/abs/1805.02442 · HTML: https://mlsanity.com/paper/1805.02442/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

## Built on by (most cited first, 5 of 5)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://mlsanity.com/paper/2112.02721/) (2021)
- [From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds?](https://mlsanity.com/paper/2305.10568/) (2023)
- [Noun2Verb: Probabilistic frame semantics for word class conversion](https://mlsanity.com/paper/2205.06321/) (2022)
- [Willkommens-Merkel, Chaos-Johnson, and Tore-Klose: Modeling the Evaluative Meaning of German Personal Name Compounds](https://mlsanity.com/paper/2404.04031/) (2024)

## Abstract (arXiv)

Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.