SemEval-2013 Task 4: Free Paraphrases of Noun Compounds
Not on the map. Why?
Abstract (arXiv)
In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results. The task is to capture some of the meaning of English noun compounds via paraphrasing. Given a two-word noun compound, the participating system is asked to produce an explicitly ranked list of its free-form paraphrases. The list is automatically compared and evaluated against a similarly ranked list of paraphrases proposed by human annotators, recruited and managed through Amazon's Mechanical Turk. The comparison of raw paraphrases is sensitive to syntactic and morphological variation. The "gold" ranking is based on the relative popularity of paraphrases among annotators. To make the ranking more reliable, highly similar paraphrases are grouped, so as to downplay superficial differences in syntax and morphology. Three systems participated in the task. They all beat a simple baseline on one of the two evaluation measures, but not on both measures. This shows that the task is difficult.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Nov 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 2; 2025: 2.
11 papers.
- 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentationcited by 73
- 2022SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embeddingcited by 36
- 2021AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Modelscited by 27
- 2023From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds?cited by 12
- 2024Semantics of Multiword Expressions in Transformer-Based Models: A Surveycited by 9
- 2024Creativity in AI: Progresses and Challengescited by 7
- 2021Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun Phrasescited by 3
- 2025SemEval-2025 Task 1: AdMIRe -- Advancing Multimodal Idiomaticity Representationcited by 3
- 2025Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual Combination with Property Typecited by 2
- 2022"Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compoundscited by 1
- 2021Tapping BERT for Preposition Sense Disambiguationcited by 0
- 2025SemEval-2025 Task 1: AdMIRe -- Advancing Multimodal Idiomaticity Representation19 Mar 2025
- 2025Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual Combination with Property Type10 Feb 2025
- 2024Creativity in AI: Progresses and Challenges22 Oct 2024
- 2024Semantics of Multiword Expressions in Transformer-Based Models: A Survey27 Jan 2024
- 2023From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds?17 May 2023
- 2022"Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compounds24 Oct 2022
- 2022SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding21 Apr 2022
- 2021Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun Phrases15 Dec 2021
- 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation6 Dec 2021
- 2021Tapping BERT for Preposition Sense Disambiguation27 Nov 2021
- 2021AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models9 Sep 2021