Abstract
We provide the first computational treatment of fused-heads constructions (FHs), focusing on the numeric fused-heads (NFHs). FHs constructions are noun phrases 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 FHs 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 data set and a highly accurate method for NFH identification; (2) a 10k examples (1 M tokens) crowd-sourced data set of NFH resolution; and (3) a neural baseline for the NFH resolution task. We release our code and data set, to foster further research into this challenging problem.
| Original language | English |
|---|---|
| Pages (from-to) | 519-535 |
| Number of pages | 17 |
| Journal | Transactions of the Association for Computational Linguistics |
| Volume | 7 |
| DOIs | |
| State | Published - 2019 |
Bibliographical note
Publisher Copyright:© 2019 Association for Computational Linguistics. Distributed under a CC-BY 4.0 license.
Funding
We would like to thank Reut Tsarfaty and the Bar-Ilan University NLP lab for the fruitful conversation and helpful comments. The work was supported by the Israeli Science Foundation (grant 1555/15) and the German Research Foundation via the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1).
| Funders | Funder number |
|---|---|
| DIP | DA 1600/1-1 |
| German-Israeli Project Cooperation | |
| Reut Tsarfaty | |
| Deutsche Forschungsgemeinschaft | |
| Bar-Ilan University | |
| Israel Science Foundation | 1555/15 |