# Adposition and Case Supersenses v2.6: Guidelines for English

arXiv: https://arxiv.org/abs/1704.02134 · HTML: https://mlsanity.com/paper/1704.02134/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2017.

## Built on by (most cited first, 4 of 4)

- [DirectProbe: Studying Representations without Classifiers](https://mlsanity.com/paper/2104.05904/) (2021)
- [PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English](https://mlsanity.com/paper/2110.12243/) (2021)
- [Hindi-Urdu Adposition and Case Supersenses v1.0](https://mlsanity.com/paper/2103.01399/) (2021)
- [MASALA: Modelling and Analysing the Semantics of Adpositions in Linguistic Annotation of Hindi](https://mlsanity.com/paper/2205.03955/) (2022)

## Abstract (arXiv)

This document offers a detailed linguistic description of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al., 2018), an inventory of 52 semantic labels ("supersenses") that characterize the use of adpositions and case markers at a somewhat coarse level of granularity, as demonstrated in the STREUSLE corpus (https://github.com/nert-nlp/streusle/ ; version 4.5 tracks guidelines version 2.6). Though the SNACS inventory aspires to be universal, this document is specific to English; documentation for other languages will be published separately. Version 2 is a revision of the supersense inventory proposed for English by Schneider et al. (2015, 2016) (henceforth "v1"), which in turn was based on previous schemes. The present inventory was developed after extensive review of the v1 corpus annotations for English, plus previously unanalyzed genitive case possessives (Blodgett and Schneider, 2018), as well as consideration of adposition and case phenomena in Hebrew, Hindi, Korean, and German. Hwang et al. (2017) present the theoretical underpinnings of the v2 scheme. Schneider et al. (2018) summarize the scheme, its application to English corpus data, and an automatic disambiguation task. Liu et al. (2021) offer an English Lexical Semantic Recognition tagger that includes SNACS labels in its output. This documentation can also be browsed alongside corpus data on the Xposition website (Gessler et al., 2022): http://www.xposition.org/