# The correlation between nativelike selection and prototypicality: a multilingual onomasiological case study using semantic embedding

arXiv: https://arxiv.org/abs/2405.13529 · HTML: https://mlsanity.com/paper/2405.13529/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 131 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Visualizing and Measuring the Geometry of BERT](https://mlsanity.com/paper/1906.02715/) (2019)
- [N-LTP: An Open-source Neural Language Technology Platform for Chinese](https://mlsanity.com/paper/2009.11616/) (2020)
- [What does it mean to be language-agnostic? Probing multilingual sentence encoders for typological properties](https://mlsanity.com/paper/2009.12862/) (2020)
- [Experiments on Generalizability of BERTopic on Multi-Domain Short Text](https://mlsanity.com/paper/2212.08459/) (2022)
- [Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings](https://mlsanity.com/paper/2204.03251/) (2022)
- [A State of the Art of Word Sense Induction: A Way Towards Word Sense Disambiguation for Under-Resourced Languages](https://mlsanity.com/paper/1310.1425/) (2013)

## Abstract (arXiv)

In native speakers' lexical choices, a concept can be more readily expressed by one expression over another grammatical one, a phenomenon known as nativelike selection (NLS). In previous research, arbitrary chunks such as collocations have been considered crucial for this phenomenon. However, this study examines the possibility of analyzing the semantic motivation and deducibility behind some NLSs by exploring the correlation between NLS and prototypicality, specifically the onomasiological hypothesis of Grondelaers and Geeraerts (2003, Towards a pragmatic model of cognitive onomasiology. In Hubert Cuyckens, Ren'e Dirven & John R. Taylor (eds.), Cognitive approaches to lexical semantics, 67-92. Berlin: De Gruyter Mouton). They hypothesized that "[a] referent is more readily named by a lexical item if it is a salient member of the category denoted by that item". To provide a preliminary investigation of this important but rarely explored phenomenon, a series of innovative methods and procedures, including the use of semantic embedding and interlingual comparisons, is designed. Specifically, potential NLSs are efficiently discovered through an automatic exploratory analysis using topic modeling techniques, and then confirmed by manual inspection through frame semantics. Finally, to account for the NLS in question, cluster analysis and behavioral profile analysis are conducted to uncover a language-specific prototype for the Chinese verb shang 'harm', providing supporting evidence for the correlation between NLS and prototypicality.
