SuperSim: a test set for word similarity and relatedness in Swedish
Not on the map. Why?
Abstract (arXiv)
Language models are notoriously difficult to evaluate. We release SuperSim, a large-scale similarity and relatedness test set for Swedish built with expert human judgments. The test set is composed of 1,360 word-pairs independently judged for both relatedness and similarity by five annotators. We evaluate three different models (Word2Vec, fastText, and GloVe) trained on two separate Swedish datasets, namely the Swedish Gigaword corpus and a Swedish Wikipedia dump, to provide a baseline for future comparison. We release the fully annotated test set, code, baseline models, and data.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 33 references that are in the map. First posted on arXiv 12 Apr 2021.
Builds on
12 of 33 references.
References · 12
- 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2014SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimationcited by 170
- 2020Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similaritycited by 38
- 2018Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Changecited by 35
- 2019Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Changecited by 32
- 2016Problems With Evaluation of Word Embeddings Using Word Similarity Taskscited by 25
- 2016Correlation-based Intrinsic Evaluation of Word Vector Representationscited by 5
- 2020Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corporacited by 3
- 2020Exploring Swedish & English fastText Embeddings for NER with the Transformercited by 1
Built on by
Citing papers per year: 2023: 1.
1 paper.