Where's My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution
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Abstract (arXiv)
We provide the first computational treatment of fused-heads constructions (FH), focusing on the numeric fused-heads (NFH). FHs constructions are noun phrases (NPs) in which the head noun is missing and is said to be `fused' with its dependent modifier. This missing information is implicit and is important for sentence understanding. The missing references are easily filled in by humans but pose a challenge for computational models. We formulate the handling of FH as a two stages process: identification of the FH construction and resolution of the missing head. We explore the NFH phenomena in large corpora of English text and create (1) a dataset and a highly accurate method for NFH identification; (2) a 10k examples (1M tokens) crowd-sourced dataset of NFH resolution; and (3) a neural baseline for the NFH resolution task. We release our code and dataset, in hope to foster further research into this challenging problem.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 May 2019.
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Citing papers per year: 2021: 6; 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
11 papers.
- 2021Representing Numbers in NLP: a Survey and a Visioncited by 83
- 2021Contrastive Explanations for Model Interpretabilitycited by 75
- 2022Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluationcited by 67
- 2023Prompting with Pseudo-Code Instructionscited by 14
- 2021Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Contextcited by 5
- 2023CARE: Extracting Experimental Findings From Clinical Literaturecited by 5
- 2024Superlatives in Context: Modeling the Implicit Semantics of Superlativescited by 5
- 2021BERT Has Uncommon Sense: Similarity Ranking for Word Sense BERTologycited by 2
- 2021Great Service! Fine-grained Parsing of Implicit Argumentscited by 0
- 2021Meaning Representation of Numeric Fused-Heads in UCCAcited by 0
- 2026LLMs Infer Cultural Context but Fail to Apply It When Respondingcited by 0
- 2026LLMs Infer Cultural Context but Fail to Apply It When Responding16 Jun 2026
- 2024Superlatives in Context: Modeling the Implicit Semantics of Superlatives31 May 2024
- 2023CARE: Extracting Experimental Findings From Clinical Literature16 Nov 2023
- 2023Prompting with Pseudo-Code Instructions19 May 2023
- 2022Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation15 Feb 2022
- 2021Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context16 Dec 2021
- 2021BERT Has Uncommon Sense: Similarity Ranking for Word Sense BERTology20 Sep 2021
- 2021Meaning Representation of Numeric Fused-Heads in UCCA4 Jun 2021
- 2021Great Service! Fine-grained Parsing of Implicit Arguments4 Jun 2021
- 2021Representing Numbers in NLP: a Survey and a Vision24 Mar 2021
- 2021Contrastive Explanations for Model Interpretability2 Mar 2021