WiDiD: What is Done is Done

This is the official repository for our paper Studying Word Meaning Evolution through Incremental Semantic Shift Detection: A Case Study of Italian Parliamentary Speeches

Overview

This repository contains the code and data used to implement and evaluate WiDiD, as well as the code for analyzing and visualizing semantic shifts in a given corpus.

WiDiD is an incremental approach to Semantic Shift Detection, designed for tracing the evolution of word meanings over time in a diachronic corpus.

Figure: An example of semantic shift in the word abuse over time

Table of Contents

• Introduction
• Getting Started
• Case Study
• Abstract
• References

Introduction

Words are malleable, and their meanings continuously evolve due to various factors such as social practices, events, and political circumstances. Understanding these semantic shifts is essential for linguists, historians, and lexicographers. The WiDiD approach provides a computational method for capturing these changes and analyzing the evolution of individual word senses.

Getting Started

Before you begin, ensure you have met the following requirements:

• Python 3.8+
• Required Python packages (listed in requirements.txt)

To install the required packages, you can use pip:

pip install -r requirements.txt

Evaluation

Download data

bash sbatch/semeval-english-download.sh datasets
bash sbatch/semeval-swedish-download.sh datasets
bash sbatch/semeval-latin-download.sh datasets
bash sbatch/semeval-german-download.sh datasets
bash sbatch/rushifteval-russian-download.sh datasets tokenization
bash sbatch/diacrita-italian-download.sh datasets
bash sbatch/lscdiscovery-spanish-download.sh datasets

Tokenization

bash sbatch/semeval-english-tokenization.sh datasets tokenization
bash sbatch/semeval-german-tokenization.sh datasets tokenization
bash sbatch/semeval-latin-tokenization.sh datasets tokenization
bash sbatch/semeval-swedish-tokenization.sh datasets tokenization
bash sbatch/diacrita-italian-tokenization.sh datasets tokenization
bash sbatch/lsdiscovery-spanish-tokenization.sh datasets tokenization

Embedding extraction

bash sbatch/semeval-english-extraction.sh tokenization embeddings datasets
bash sbatch/semeval-german-extraction.sh tokenization embeddings datasets
bash sbatch/semeval-latin-extraction.sh tokenization embeddings datasets
bash sbatch/semeval-swedish-extraction.sh tokenization embeddings datasets
bash sbatch/diacrita-italian-extraction.sh tokenization embeddings datasets
bash sbatch/lscdiscovery-spanish-extraction.sh tokenization embeddings datasets
bash sbatch/rushifteval-russian-extraction.sh tokenization embeddings datasets

Clustering

bash sbatch/semeval-english-clustering.sh embeddings labels datasets
bash sbatch/diacrita-italian-clustering.sh embeddings labels datasets
bash sbatch/rushifteval-russian-clustering.sh embeddings labels datasets
bash sbatch/semeval-german-clustering.sh embeddings labels datasets
bash sbatch/semeval-latin-clustering.sh embeddings labels datasets
bash sbatch/semeval-swedish-clustering.sh embeddings labels datasets
bash sbatch/lscdiscovery-spanish-clustering.sh embeddings labels datasets

LSC measuring

bash sbatch/semeval-english-lsc-measuring.sh embeddings labels scores datasets
bash sbatch/diacrita-italian-lsc-measuring.sh embeddings labels scores datasets
bash sbatch/rushifteval-russian-measuring.sh" embeddings labels scores datasets
bash sbatch/semeval-german-lsc-measuring.sh embeddings labels scores datasets
bash sbatch/semeval-latin-lsc-measuring.sh embeddings labels scores datasets
bash sbatch/semeval-swedihs-lsc-measuring.sh embeddings labels scores datasets
bash sbatch/lscdiscovery-spanish-lsc-measuring.sh embeddings labels scores datasets

Case Study

Download data

unzip data.zip

Case-study application

python case-study-application.py

Case-study visualization.py

python case-study-visualization.py

Abstract

The study of semantic shifts, that is, of how words change meaning as a consequence of social practices, events and political circumstances, is relevant in Natural Language Processing, Linguistics, and Social Sciences. The increasing availability of large diachronic corpora and advance in computational semantics have accelerated the development of computational approaches to detecting such shifts. In this paper, we introduce a novel approach to tracing the evolution of word meaning over time. Our analysis focuses on gradual changes in word semantics and relies on an incremental approach to semantic shift detection (SSD) called WiDiD. WiDiD leverages scalable and evolutionary clustering of contextualised word embeddings to detect semantic shifts and capture temporal transactions in word meanings. Existing approaches to SSD (a) significantly simplify the semantic shift problem to cover change between two (or a few) time points, and (b) consider the existing corpora as static. We instead treat SSD as an organic process in which word meanings evolve across tens or even hundreds of time periods as the corpus is progressively made available. This results in an extremely demanding task that entails a multitude of intricate decisions.

We demonstrate the applicability of this incremental approach on a diachronic corpus of Italian parliamentary speeches spanning eighteen distinct time periods. We also evaluate its performance on seven popular labelled benchmarks for SSD across multiple languages. Empirical results show that our results are at least comparable to state-of-the-art approaches, while outperforming the state-of-the-art for certain languages.

References

@Article{periti2023word,
    author    = {Francesco Periti and Sergio Picascia and Stefano Montanelli and Alfio Ferrara and Nina Tahmasebi},
  title     = {{Studying Word Meaning Evolution through Incremental Semantic Shift Detection}},
  journal   = {Language Resources and Evaluation},
  year      = {2024},
  volume    = {},
  number    = {},
  pages     = {},
  month     = sep,
  issn      = {1574-0218},
  url       = {https://doi.org/10.1007/s10579-024-09769-1},
  doi       = {10.1007/s10579-024-09769-1}
}

@inproceedings{periti2022done,
    title = {{What is Done is Done: an Incremental Approach to Semantic Shift Detection}},
    author = "Periti, Francesco  and
              Ferrara, Alfio  and
              Montanelli, Stefano  and
              Ruskov, Martin",
    booktitle = "Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.lchange-1.4",
    doi = "10.18653/v1/2022.lchange-1.4",
    pages = "33--43"
}

@Article{periti2024survey,
    author = {Periti, Francesco and Montanelli, Stefano},
    title = {{Lexical Semantic Change through Large Language Models: a Survey}},
    year = {2024},
    issue_date = {November 2024},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    volume = {56},
    number = {11},
    issn = {0360-0300},
    url = {https://doi.org/10.1145/3672393},
    doi = {10.1145/3672393},
    journal = {ACM Comput. Surv.},
    month = {jun},
    articleno = {282},
    numpages = {38},
    keywords = {Lexical semantics, lexical semantic change, semantic shift detection, large language models}
}