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16 results for “semantic change”
A Benchmark dataset on Semantic Change in Scholarly Publications on Disability
<p>This is a benchmark dataset for semantic shift detection in disability-related corpora, including collected title and abstract text from PubMed and ArXiv, annotation sets based on domain experts and LLMs, and extracted KGs (Wikidata entity claims). The corpus from PubMed covers the period from the 1900s to 2023, while the corpus from ArXiv covers the period from the 1990s to 2023. The corpus was filtered based on 16 disability-related target words. In the annotation sets, '1' indicates that a semantic shift occurred for a target word, while '0' indicates the opposite. In particular, the LLM-based annotation sets include their generated text, and we used the Llama2 and GPT-4 models. '7b' refers to the parameter size of the Llama2 model. Graph_data.zip contains Wikidata entity claims.</p>
Data for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>This dataset accompanies a replication package provided for a study on Semantic Versioning for Ansible Galaxy roles.</p> <p>The replication package is available at https://github.com/ROpdebee/ansible_semver_ext_replication</p>
Benchmark for the Evaluation of Lexical Semantic Change Detection for Ancient Greek
<p>This repository contains a benchmark of Ancient Greek lemmas which underwent semantic change. It is meant as a support for the evaluation of methods detecting lexical semantic change in Ancient Greek. It was created at the University of Groningen, The Netherlands. A publication will follow soon.</p> <p> </p> <p><strong>1. Overview of the repository</strong></p> <p>This benchmark was created by retrieving and selecting from existing scholarship cases of lexemes which underwent semantic change. The evaluation items are 44 Ancient Greek lemmas, accompanied by the following information (see the column headers in the CSV file):</p> <ul> <li><strong>reference:</strong> the literature source of information about the change;</li> <li><strong>which_change: </strong>an explanation of the change in meaning. NB: the older meaning(s) do not necessarily disappear after the change, but it can happen that the new meaning(s) are added to the existing one(s), increasing the polysemy of the lemma;</li> <li><strong>when_changed: </strong>information about the work(s) or time period in which the change was first recorded; this kind of information was not always available or precise;</li> <li><strong>christian_change:</strong> whether the change is triggered by social, religious, or cultural changes related to the spread of Christianity, according to the scholarship.</li> </ul> <p> </p> <p><strong>2. References</strong></p> <p>The literature used to build this benchmark is the following:</p> <p> BUCK, Carl Darling. A dictionary of selected synonyms in the principal Indo-European languages. University of Chicago Press, 1949.</p> <p> FINKELBERG, Aryeh. "On the History of the Greek ΚΟΣΜΟΣ." Harvard Studies in Classical Philology (1998): 103-136.</p> <p> GINGRICH, F. Wilbur. "The Greek New Testament as a landmark in the course of semantic change." <em>Journal of Biblical Literature</em> (1954): 189-196.</p> <p> HORKY, Phillip Sidney. "When did Kosmos become the Kosmos." <em>Cosmos in the Ancient World</em> (2019): 22-41.</p> <p> LURAGHI, Silvia. "The verb aréskein in Ancient Greek: Constructions and semantic change." <em>Acta Linguistica Petropolitana. Труды института лингвистических исследований</em> 18-1 (2022): 226-245.</p> <p> </p> <p>These dictionaries of Ancient Greek were also used to double-check the instances of change:</p> <p> LIDDELL, Henry George, and Robert Scott. <em>A Greek-English Lexicon</em>. revised and augmented throughout by. Sir Henry Stuart Jones. with the assistance of. Roderick McKenzie. Oxford. Clarendon Press. 1940.</p> <p> ROCCI, Lorenzo.<em> Vocabolario greco-italiano</em>. Roma. Società editrice Dante Alighieri. 1939.</p> <p> SLUITER, Ineke, and Lucien van Beek, and Ton Kessels, and Albert Rijksbaron. <em>Woordenboek Grieks/Nederlands</em>. 2024. <a href="https://woordenboekgrieks.nl/" target="_blank" rel="noopener">https://woordenboekgrieks.nl/</a></p> <p> </p> <p><strong>3. Acknowledgements</strong></p> <div>This work was partially supported by the Young Academy Groningen through the PhD scholarship of Silvia Stopponi.<br> <br>We acknowledge the financial support of Anchoring Innovation. Anchoring Innovation is the Gravitation Grant research agenda of the Dutch National Research School in Classical Studies, OIKOS. It is financially supported by the Dutch ministry of Education, Culture and Science (NWO project number 024.003.012). For more information about the research programme and its results, see the website <a href="https://www.anchoringinnovation.nl/">www.anchoringinnovation.nl</a>.</div> <div> <p> </p> <p><strong>4. How to cite</strong></p> </div> <div>Until there is no publication about this benchmark, please cite the resource as:</div> <div>Silvia Stopponi, Saskia Peels-Matthey, Malvina Nissim (2024), <em>Benchmark for the Evaluation of Lexical Semantic Change Detection Measures in Ancient Greek</em>, DOI: 10.5281/zenodo.13364555.</div> <div> </div> <div> </div>
Swedish Test Data for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection
<p>This data collection contains the Swedish test data for <a href="https://competitions.codalab.org/competitions/20948">SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection:</a></p> <p>- a Swedish text corpus pair (`corpus1/`, `corpus2/`)<br> - 31 lemmas which have been annotated for their lexical semantic change between the two corpora (`targets.txt`)<br> - the annotated binary change scores of the targets for subtask 1, and their annotated graded change scores for subtask 2 (`truth/`)</p> <p>We sample from the KubHist2 corpus, digitized by the National Library of Sweden, and available through the Språkbanken corpus infrastructure Korp (<a href="https://www.researchgate.net/profile/Markus_Forsberg/publication/266352576_Korp_-_the_corpus_infrastructure_of_Sprakbanken/links/55bf1ee008aed621de121ba3/Korp-the-corpus-infrastructure-of-Sprakbanken.pdf">Borin et al., 2012</a>). The full corpus is available through a CC BY (attribution) license. Each word for which the lemmatizer in the Korp pipelien has found a lemma is replaced with the lemma. In cases where the lemmatizer cannot find a lemma, we leave the word as is (i.e., unlemmatized, no lower-casing). KubHist contains very frequent OCR errors, especially for the older data.More detail about the properties and quality of the Kubhist corpus can be found in (<a href="https://www.diva-portal.org/smash/get/diva2:1358014/FULLTEXT01.pdf#page=28">Adesam et al., 2019</a>).</p> <p>Lars Borin, Markus Forsberg, and Johan Roxendal. "Korp-the corpus infrastructure of Språkbanken." <em>LREC</em>. 2012.</p> <p>Adesam, Yvonne, Dana Dannélls, and Nina Tahmasebi. "Exploring the Quality of the Digital Historical Newspaper Archive KubHist." <em>DHN</em>. 2019.</p> <p>__Corpus 1__</p> <p>- based on: <a href="https://spraakbanken.gu.se/korp/?mode=kubhist">Kubhist2</a><br> - language: Swedish<br> - time covered: 1790-1830<br> - size: ~71 million tokens<br> - format: lemmatized, sentence length > 9 (before removal of punctuation), no punctuation, sentences randomly shuffled<br> - encoding: UTF-8<br> - note: contains frequent OCR errors</p> <p>__Corpus 2__</p> <p>- based on: <a href="https://spraakbanken.gu.se/korp/?mode=kubhist">Kubhist2</a><br> - language: Swedish<br> - time covered: 1895-1903<br> - size: ~111 million tokens<br> - format: lemmatized, sentence length > 9 (before removal of punctuation), no punctuation, sentences randomly shuffled<br> - encoding: UTF-8<br> - note: contains OCR errors</p> <p>Besides the official lemma version of the corpora for SemEval-2020 Task 1 we also provide the raw token version (`corpus1/token/`, `corpus2/token/`). It contains the raw sentences in the same order as in the lemma version. Find more information on the data and SemEval-2020 Task 1 in the paper referenced below.</p> <p> </p> <p>Reference:</p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi.<a href="https://competitions.codalab.org/competitions/20948">SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>. To appear in SemEval@COLING2020.</p>
A list of Swedish words that have experienced historical semantic changes
<p>This list contains a set of Swedish words that have experienced semantic change during the past centuries. The list has been collected during the VR funded project <a href="https://languagechange.org/">Towards Computational Lexical Semantic Change Detection</a>, ( 2018-01184) and is a work in progress. Because the work is currently on pause, we have chosen to release the list as is in the hope of facilitating collaboration and use in other research.</p> <p>The list has four columns in the following format:</p> <pre><code>Ord*, Vilken betydelseförändring har skett*, När skedde förändringen, Källor (exv SAOL) word, what change occurred, when the change occur, reference</code></pre> <p><br> Not all fields are filled for every word. Where there are multiple change periods, there are multiple lines with empty values for word and what change occurred, see the example with <em>egendomlig </em>below.</p> <pre><code>egendomlig,som utgör (ngns) egendom > karaktäristisk (positiv) > speciell (negativt!!),"A. Sen 1600tal, ",SAOB ,,B. Sen 1850, ,,"C. Sen ??, efter 190",</code></pre> <p>The .xlsx file contains links to the references.</p> <p>The resources are freely available for education, research and other non-commercial purposes.</p> <p> </p>
JeSemE models for lexical semantic change
<p>Models for diachronic lexical semantics used by the <a href="http://jeseme.org">Jena Semantic Explorer (JeSemE)</a> web site described in our <a href="http://aclweb.org/anthology/C18-2003">COLING 2018 paper "JeSemE: A Website for Exploring Diachronic Changes in Word Meaning and Emotion"</a>.</p> <p>Also described and applied in Johannes Hellrich's Ph.D. thesis "Word Embeddings: Reliability & Semantic Change" who was funded by the Deutsche Forschungsgemeinschaft (DFG) within the graduate school "The Romantic Model" (GRK 2041/1).</p> <p>One ZIP file per corpus, each containing several CSV files:</p> <ul> <li>CHI.csv with χ<sup>2 </sup>word association values (structure: word-id, word-id, time, value)</li> <li>EMBEDDING.csv with SVD-PPMI word embeddings (aligned; structure: word-id, time, values)</li> <li>EMOTION.csv with VAD word emotion values (structure: word-id, time, values)</li> <li>FREQUENCY.csv with relative word frequency values (structure: word-id, time, value)</li> <li>PPMI.csv with PPMI<sup> </sup>word association values (structure: word-id, word-id, time, value)</li> <li>SIMILARITY.csv with word embedding derived word similarity values (structure: word-id, word-id, time, value)</li> <li>WORDIDS.csv mapping words to their corpus specific IDs</li> </ul> <p>Corpora are:</p> <ul> <li> <p>coha: Corpus of Historical American English</p> </li> <li> <p>dta: Deutsches Textarchiv 'German Text Archive'</p> </li> <li> <p>google_fiction: Google Books N-Gram corpus, English fiction subcorpus</p> </li> <li> <p>google_german: Google Books N-Gram corpus, German subcorpus</p> </li> <li> <p>rsc: Royal Society Corpus </p> </li> </ul>
Breaking Bad? Semantic Versioning and Impact of Breaking Changes in Maven Central (Dataset)
<p>The content presented in this repository accompanies the paper "Breaking Bad? Semantic Versioning and Impact of Breaking Changes in Maven Central" authored by Lina Ochoa, Thomas Degueule, Jean-Rémy Falleri, and Jurgen Vinju. The paper was submitted and accepted in the Journal of Empirical Software Engineering (EMSE'21). This study is an external and differentiated replication study of the paper <a href="https://jstvssr.github.io/assets/pdf/semantic-versioning-maven.pdf">"Semantic Versioning and Impact of Breaking Changes in the Maven Repository"</a> presented by Steven Raemaekers, Arie van Deursen, and Joost Visser.</p> <p><strong>Content</strong></p> <ul> <li><strong>README.md: </strong>document with the main description to start exploring the bundle.</li> <li><strong>data.zip: </strong>contains the datasets used within the study. These datasets must be used to get the same results like the ones presented in the article.</li> <li><strong>maven-api-dataset.zip:</strong> contains the code used to generate the datasets and to analyse the obtained results. Check the README.md file within this bundle for more information.</li> </ul> <p><strong>Relevant Links</strong></p> <ul> <li><strong>maven-api-dataset repository:</strong> <a href="https://github.com/tdegueul/maven-api-dataset">https://github.com/tdegueul/maven-api-dataset</a></li> <li><strong>maracas repository:</strong> <a href="https://github.com/crossminer/maracas">https://github.com/crossminer/maracas</a></li> <li><strong>Companion webpage:</strong> <a href="https://crossminer.github.io/maracas/2021/08/16/emse21/">https://crossminer.github.io/maracas/2021/08/16/emse21/</a></li> </ul>
Lexical Semantic Change Cause-Type-Definitions Benchmark
<p>The Lexical Semantic Change Cause-Type-Definitions (LSC-CTD) Benchmark is a digitised dataset that builds on and extends the Blank's seminal 1997 taxonomy of semantic change. This collection categorises 657 instances of linguistic evolution across the vocabulary of the Romance languages, with additional entries of German and English instances. Each entry is accompanied by a new pair (Old and New Meaning) of english definitions, manually curated by a historical linguist.</p> <p>The dataset includes a detailed classification of causes of change such as semantic wear, lexical gap, orphaned word, lexical complexity, atypical actant structure, frame, socio-cultural change, abstract concept, atypical part of speech, new concept, taboo, expressivity and prototype. It also includes types of semantic shift as classified by Blank, i.e. specialisation, generalisation, co-hyponymous transfer, auto-antonym, metaphor, antiphrasis, metonymy, auto-converse, ellipsis, folk etymology, analogy, meaning dilution, meaning reinforcement and doubtful cases. </p> <p><br><strong>Reference</strong></p> <p>The accompanying paper where this resource is described in detail will be published at ACL 2024.<br><br><span>Pierluigi Cassotti, Stefano De Pascale, and Nina Tahmasebi. 2024. <a href="https://aclanthology.org/2024.acl-long.249">Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types</a>. In <em>Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</em>, pages 4539–4553, Bangkok, Thailand. Association for Computational Linguistics.</span></p>
Main and extended tables for the 207-word Swadesh list of Early Sranan and Modern Sranan with parts of speech, semantic categories, source languages and semantic and lexical changes
<p>The dataset was made for the purposes of the author's master thesis, titled <a href="https://repozitorij.uni-lj.si/Dokument.php?id=170462&lang=slv">"Socio-Cultural Motivations for the Acquisition of Lexical Items in Sranan Tongo’s Core Vocabulary"</a>. The dataset includes two worksheets. The first is titled "Main table", and it includes all the data, where each Swadesh gloss (1 to 207) is assigned one ID (No., first column), even if there are multiple Modern Sranan (MSr) equivalents. The second worksheet, titled "Extended table", includes additional IDs (No., first column) by hyphenating, so that each MSr equivalent has its separate ID number (e. g. gloss numbered 2 has 3 MSr equivalents, so these are now numbered 2-1, 2-2, and 2-3, respectively). <br> This allowed the author to also make a clearer distinction according to source languages, as the MSr equivalents for the same gloss sometimes come from different source languages. More about the methodology of the tables and their importance for the research is available in the thesis, available <a href="https://repozitorij.uni-lj.si/Dokument.php?id=170462&lang=slv">at this link</a>. </p>
SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
<p><strong>Authors</strong></p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, and Nina Tahmasebi</p> <p><strong>Description</strong></p> <p>This data collection contains the <strong>post-evaluation</strong> data for <a href="https://languagechange.org/semeval">SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>:</p> <ul> <li>the starting kit to download data, and examples for competing in the CodaLab challenge including baselines</li> <li>the true binary change scores of the targets for Subtask 1, and their true graded change scores for Subtask 2 (<code>test_data_truth/</code>),</li> <li>the scoring program used to score submissions against the true test data in the evaluation and post-evaluation phase (<code>scoring_program/</code>),</li> <li>the results of the evaluation phase including <ul> <li>the final rankings of the participating teams by their best submission (<code>results/rankings_teams.csv</code>),</li> <li>the submitted files of each team (<code>results/submissions/</code>),</li> <li>an overview of the results for each submission ordered by team (<code>results/submissions_results.csv</code>),</li> <li>analysis plots (<code>plots/</code>) displaying the results: <ul> <li>under <code>per_target/</code> we provide the gold change scores and the normalized prediction error of target words plotted against their frequency and polysemy statistics,</li> <li>under <code>per_team/</code> we provide the model predictions from the best submission per team (per subtask) plotted against frequency/polysemy statistics and performance on gold data (gray lines give the correlation with the respective variable in the gold data); we also provide plots of visualizing the teams' prediction similarities.</li> </ul> </li> </ul> </li> </ul> <p>Some remarks:</p> <ul> <li>the paper referenced below remains the only source for the rankings between teams,</li> <li>some teams were disqualified, and are thus removed from the analyses and the rankings present in the paper,</li> <li>some teams have changed names, resulting in a discrepancy between team names under <code>results/</code> and team names in the paper. The paper contains a key to match old names with new names.</li> </ul> <p><strong>Test Data </strong>for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection can be found using the links below:</p> <ul> <li><a href="https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd-eng/">English</a></li> <li><a href="https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd-ger/">German</a></li> <li><a href="https://zenodo.org/record/3734089">Latin</a></li> <li><a href="https://zenodo.org/record/3730550">Swedish</a></li> </ul> <p>Please find more information on the provided data in the paper referenced below.</p> <p><strong>Reference</strong></p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi. 2020. <a href="https://languagechange.org/semeval">SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>. SemEval@COLING2020.</p> <p>The resources are freely available for education, research and other non-commercial purposes.</p> <pre><code>@inproceedings{schlechtweg2020semeval, title = "{S}em{E}val-2020 {T}ask 1: {U}nsupervised {L}exical {S}emantic {C}hange {D}etection", author = "Schlechtweg, Dominik and McGillivray, Barbara and Hengchen, Simon and Dubossarsky, Haim and Tahmasebi, Nina", booktitle = "To appear in Proceedings of the 14th International Workshop on Semantic Evaluation", year = "2020", address = "Barcelona, Spain", publisher = "Association for Computational Linguistics"}</code></pre> <p> </p>
GlossReader at LSCDiscovery: Train to Select a Proper Gloss in English -- Discover Lexical Semantic Change in Spanish
<pre>Precomputed vectors for the GlossReader system. LSCDiscovery Competition: https://codalab.lisn.upsaclay.fr/competitions/2243. </pre>
Mining the UK Web Archive for Semantic Change Detection (Dataset)
<p>The dataset that was used and released with the RANLP 2019 paper, titled "Mining the UK Web Archive for Semantic Change Detection" (see <a href="https://github.com/adtsakal/Semantic_Change">https://github.com/adtsakal/Semantic_Change</a>). It contains annual word2vec representations of more than 47K words over the period 2000-2013, along with a list of 65 words with known semantic change over the same time period. </p>
Sampled sentence pairs from SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
<p>Each dataset consists of samples, containing two sentences with positions of one of the given target words. In every sample, first sentence is taken from corpus1 and second from corpus2. Initial sentences were taken from https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd/. </p>
Replication Package for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>Replication package for our analysis of Semantic Versioning in Ansible Galaxy role repositories.</p> <p>This replication package consists of three parts:</p> <ul> <li> <p>Classification Model: Contains Jupyter notebooks used to train and evaluate a Random Forest classification model based on structural features. Training and evaluation data is included.</p> </li> <li> <p>Quantitative Notebooks: Contains Jupyter notebooks used to perform quantitative analyses of versions and changes.</p> </li> <li> <p>data: CSV files of the data used in the Quantitative Notebooks, and the source data for the classification model. Should be downloaded separately fromthe classification model. Should be downloaded separately from <a href="https://doi.org/10.5281/zenodo.4991955">https://doi.org/10.5281/zenodo.4991955</a>.</p> </li> </ul> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license. The source code is under the GNU General Public License.</p>
Diachronic semantics: changes of meaning of words over time and the consequences for keeping classification systems up to date
<p>Meanings of words in a natural language are changing over time under the influences of different factors. Words adapt to new meanings, lose old meanings, rearrange current meanings and change some parts of previous meanings, etc. The language as a living organism needs to be able to accept and adapt to those changes. Like natural languages, artificial languages such as classification systems or subject indexing systems have to adjust to those changes too. Linguist F. de Saussure defines language as a 'system of signs'. If we transfer this definition into the artificial language such as a classification system (e.g., Universal Decimal Classification (UDC)) and define it also as a 'system of signs' which has a vocabulary, a grammar and a syntax, we could draw parallels between phenomena which occur in natural and in artificial languages. The young linguistic discipline which deals with changes in meanings over time is called diachronic semantics and this paper explores how its mechanisms can be used to analyze the changes that occur in classification systems over time. Diachronic semantics uses various mechanisms to describe adapting and changing meanings of words. These mechanisms include: metaphor, metonymy, specialization, generalization, analogy and splitting. This paper also aims to explain the borrowing and adjusting of the theory from the field of linguistics into the field of information sciences.</p>
Ketamine's Effect Changes the Cortical Electrophysiological Activity Related to Semantic Affective Dimension of Pain
ClinicalTrials.gov study NCT03915938. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
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International Brain Laboratory public data
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OpenNeuro
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