The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage

arXiv:2606.16560 · cs.CL · Submitted 2026-06-15 · Read on arXiv

cs.CL

Submitted: 2026-06-15

Updated: 2026-09-21

Code: https://github.com/Afnan-Aloraini/Bi-Directional-Lexical-Semantic-Change-Dataset

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 36× in this paper · explained in 1 episode
• Embeddings — 36× in this paper · explained in 2 episodes
• Word Sense Disambiguation — 28× in this paper
• Lexical Semantic Change — 27× in this paper
• Benchmark — 20× in this paper · explained in 2 episodes
• Transformer — 20× in this paper · explained in 2 episodes
• Clustering — 18× in this paper · explained in 2 episodes
• Exact Sense Match — 12× in this paper
• Empirical Methods in Natural Language Processing — 7× in this paper
• F1 Score — 7× in this paper · explained in 3 episodes
• Transformer Models — 7× in this paper · explained in 2 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Corpus of Historical American English — 4× in this paper
• Fidelity — 4× in this paper · explained in 2 episodes
• Annotator Agreement — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Logistic Regression — 3× in this paper
• Random Forest — 3× in this paper
• Semantic Trajectories — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Contextual Understanding — 2× in this paper · explained in 2 episodes
• Corpus of Contemporary American English — 2× in this paper
• Data Augmentation — 2× in this paper · explained in 7 episodes
• European Language Resources Association — 2× in this paper
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Support Vector Machines — 2× in this paper
• Unified Framework — 2× in this paper · explained in 5 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment Techniques — 1× in this paper · explained in 1 episode
• Because Urban Dictionary — 1× in this paper
• Clean Corpus of Historical American English — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Few-Shot Examples for Word Sense Disambiguation — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• LSCD in Natural Language Processing — 1× in this paper
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Methods for Lexical Semantic Change Detection — 1× in this paper
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
• Temporal Analysis of Language through Neural Language Models
• Explaining and Improving BERT Performance on Lexical Semantic Change Detection
• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
• Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation
• (Chat)GPT v BERT: Dawn of Justice for Semantic Change Detection
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
• DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages
• Survey of Computational Approaches to Lexical Semantic Change

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