# Detection of Non-recorded Word Senses in English and Swedish

arXiv: https://arxiv.org/abs/2403.02285 · HTML: https://mlsanity.com/paper/2403.02285/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://mlsanity.com/paper/1808.09121/) (2018)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [Capturing Evolution in Word Usage: Just Add More Clusters?](https://mlsanity.com/paper/2001.06629/) (2020)
- [Lexical Semantic Change Discovery](https://mlsanity.com/paper/2106.03111/) (2021)
- [Word Sense Disambiguation using a Bidirectional LSTM](https://mlsanity.com/paper/1606.03568/) (2016)
- [Non-Parametric Few-Shot Learning for Word Sense Disambiguation](https://mlsanity.com/paper/2104.12677/) (2021)

## Built on by (most cited first, 3 of 3)

- [Presence or Absence: Are Unknown Word Usages in Dictionaries?](https://mlsanity.com/paper/2406.00656/) (2024)
- [AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling](https://mlsanity.com/paper/2407.04079/) (2024)
- [Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic Change Modeling](https://mlsanity.com/paper/2408.05184/) (2024)

## Abstract (arXiv)

This study addresses the task of Unknown Sense Detection in English and Swedish. The primary objective of this task is to determine whether the meaning of a particular word usage is documented in a dictionary or not. For this purpose, sense entries are compared with word usages from modern and historical corpora using a pre-trained Word-in-Context embedder that allows us to model this task in a few-shot scenario. Additionally, we use human annotations on the target corpora to adapt hyperparameters and evaluate our models using 5-fold cross-validation. Compared to a random sample from a corpus, our model is able to considerably increase the detected number of word usages with non-recorded senses.