Temporal Analysis of Language through Neural Language Models

Latent dirichlet allocation

(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568

Natural Language Processing (almost) from ScratchPreprint

Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,923

Recurrent neural network based language model

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390

Quantitative Analysis of Culture Using Millions of Digitized Books

Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., The Google Books Team, Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., et al. (2010) · Science · doi:10.1126/science.1199644 · Cited by 3,235

Linguistic Regularities in Continuous Space Word Representations

Mikolov, T., Yih, W.-T., & Zweig, G. (2013) · Cited by 2,913

Mapping authors in intellectual space: A technical overview

McCain, K. W. (1990) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199009)41:6 · Cited by 987

Quantifying the evolutionary dynamics of language

Aiden, E. L., Michel, J.-B., Jackson, J., Tang, T., & Nowak, M. A. (2007) · Nature · doi:10.1038/nature06137 · Cited by 531

Syntactic Annotations for the Google Books NGram Corpus

Lin, Y., Michel, J., Aiden, E. L., Orwant, J., Brockman, W., & Petrov, S. (2012) · Cited by 423

Learning Discriminative Projections for Text Similarity Measures

Yih, W.-T., Toutanova, K., Platt, J. C., & Meek, C. (2011) · Cited by 214

Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

(2021) · doi:10.18653/v1/2021.lchange-1 · Cited by 3

Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

(2023) · doi:10.18653/v1/2023.lchange-1 · Cited by 3

Measuring tone shifts in media coverage: An empirical application to bank financing conditions in Italy

Branzoli, N., Marchetti, S., & Tarducci, E. (2025) · International Review of Economics & Finance · doi:10.1016/j.iref.2025.104564 · Cited by 2

Identify Shifts of Word Semantics through Bayesian Surprise

Wu, Z., Li, C., Zhao, Z., Wu, F., & Mei, Q. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210040 · Cited by 3

A Word Sense Distribution-based approach for Semantic Change Prediction

Tang, X., Zhou, Y., Aida, T., Sen, P., & Bollegala, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.231 · Cited by 4

Understanding Computational Models of Semantic Change: New Insights from the Speech Community

Miletić, F., Przewozny-Desriaux, A., & Tanguy, L. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.572 · Cited by 2

Discovering emergent connections in quantum physics research via dynamic word embeddings

Frohnert, F., Gu, X., Krenn, M., & van Nieuwenburg, E. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/adb00a · Cited by 5

Semantic change in adults is not primarily a generational phenomenon

Kamath, G., Yang, M. L., Reddy, S., Sonderegger, M., & Card, D. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2426815122 · Cited by 3

Diachronic Analysis of Entities by ExploitingWikipedia Page revisions

Basile, P., Caputo, A., Lawless, S., & Semeraro, G. M. (2019) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-056-4_011 · Cited by 2

Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations

Vashisth, G., Voigt‐Antons, J., Mikhailov, M., & Roller, R. (2019) · BioNLP Workshop and Shared Task (BioNLP) · doi:10.18653/v1/w19-5037 · Cited by 2

A Longitudinal Analysis of Vocabulary Changes in Social Media

Hamoodat, H., Aswad, F., Ribeiro, E., & de Menezes, R. P. (2020) · Springer proceedings in complexity · doi:10.1007/978-3-030-40943-2_18 · Cited by 2

Monitoring geometrical properties of word embeddings for detecting the emergence of new topics.

Christophe, C., Velcin, J., Cugliari, J., Boumghar, M., & Suignard, P. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.76 · Cited by 2

Diachronic Neural Network Predictor of Word Animacy

Bochkarev, V. V., Achkeev, A. A., Shevlyakova, A. V., & Khristoforov, S. V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19496-2_16 · Cited by 2

Analysis of Semantic Changes in Japanese Words Using BERT

Kobayashi, K., Aida, T., Oka, T., & Komachi, M. (2023) · Journal of Natural Language Processing · doi:10.5715/jnlp.30.713 · Cited by 2

Swap and Predict – Predicting the Semantic Changes in Words across Corpora by Context Swapping

Aida, T., & Bollegala, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.520 · Cited by 3

Emojitalianobot and EmojiWorldBot

Monti, J., Sangati, F., Chiusaroli, F., Benjamin, M., & Sina Mansour, L. (2016) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.1811 · Cited by 2

A Method to Automatically Identify Diachronic Variation in Collocations.

García, M., & Salido, M. G. (2019) · Workshop on Computational Approaches to Historical Language Change (LChange) · doi:10.18653/v1/w19-4709 · Cited by 2

Treat the Word As a Whole or Look Inside? Subword Embeddings Model Language Change and Typology

Xu, Y., Zhang, J., & Reitter, D. (2019) · Workshop on Computational Approaches to Historical Language Change (LChange) · doi:10.18653/v1/w19-4717 · Cited by 2

Dating and Stratifying a Historical Corpus with a Bayesian Mixture Model

Hellwig, O. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-192638 · Cited by 2

The Influence of Down-Sampling Strategies on SVD Word Embedding StabilityPreprint

Hellrich, J., Kampe, B., & Hahn, U. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.06810 · Cited by 3

On the Universality of Deep Contextual Language ModelsPreprint

Bhatt, S., Goyal, P., Dandapat, S., Choudhury, M., & Sitaram, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.07140 · Cited by 4

Models of diachronic semantic change using word embeddings

Montariol, S. (2021) · theses.fr (ABES) · doi:10.70675/c08ef5d0z7732z4caeza42fz68ade712e3cd · Cited by 2

“Vaderland”, “Volk” and “Natie”: Semantic Change Related to Nationalism in Dutch Literature Between 1700 and 1880 Captured with Dynamic Bernoulli Word Embeddings

Timmermans, M., Vanmassenhove, E., & Shterionov, D. (2022) · Workshop on Computational Approaches to Historical Language Change (LChange) · doi:10.18653/v1/2022.lchange-1.13 · Cited by 1

Towards the Creation of the Filipino Wordnet: A Two-Way Approach

Samson, B. P. V., Cheng, C., Chua, U. C., Velasco, D. J., Alba, A., Pelagio, T. G., Ramirez, B. A., Bangonon, R. J., Deticio, C., Gaw, S., et al. (2023) · International Conference on Asian Language Processing · doi:10.1109/ialp61005.2023.10336981 · Cited by 1

Training and evaluation of vector models for Galician

García, M. (2024) · Language Resources and Evaluation · doi:10.1007/s10579-024-09740-0 · Cited by 1

USING NATURAL LANGUAGE PROCESSING (NLP) FOR CATEGORIZING PAPER TITLES FROM GOOGLE FORMS

Lojić, A., Mašetić, Z., & Jukić, S. (2024) · International Conference on Contemporary Theory and Practice in Construction (STEPGRAD) · doi:10.61892/stp202401059l · Cited by 1

Learning Dynamic Representations in Large Language Models for Evolving Data Streams

Srivastava, A., Bhatnagar, S., Murty, M. N., & Raman, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78169-8_16 · Cited by 1

Lexical semantic change detection: A survey of tasks, benchmarks, models, and potential impacts in digital humanities and social sciences

Chen, J. R., Chersoni, E., Schlechtweg, D., & Huang, C. (2026) · Natural language processing. · doi:10.1017/nlp.2026.10032 · Cited by 1

A Web Interface for Diachronic Semantic Search in Spanish

Gamallo, P., Rodríguez-Torres, I., & García, M. (2017) · doi:10.18653/v1/e17-3012 · Cited by 1

Analysis of literal and metaphorical senses based on diachronic word embeddings

Jia, Y., Zheng, Y., Zan, H., & Wang, Z. (2017) · International Conference on Asian Language Processing · doi:10.1109/ialp.2017.8300614 · Cited by 1

Graph-based Dynamic Word Embeddings

Lu, Y., Cheng, X., Liang, Z., & Rao, Y. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/594 · Cited by 2

Word Usage Change and the Pandemic: A Computational Analysis of Short-Term Usage Change in the Italian Reddit Community

Signoroni, E., Ježek, E., & Sprugnoli, R. (2022) · Italian Journal of Computational Linguistics · doi:10.4000/ijcol.1076 · Cited by 1

Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language Models

Hoeken, S., Alaçam, Ö., Fokkens, A. S., & Sommerauer, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.237 · Cited by 1

Efficient Continue Training of Temporal Language Model with Structural Information

Su, Z., Li, J., Zhang, Z., Zhou, Z., & Zhang, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.418 · Cited by 5

A longitudinal study about gradual changes in the Iranian Online Public Sphere pre and post of ‘Mahsa Moment’: Focusing on Twitter

Jafari, S., Fathi, A., Hajizadegan, A., Kazemeini, A., & Eetemadi, S. (2023) · Workshop on Computational Approaches to Historical Language Change (LChange) · doi:10.18653/v1/2023.lchange-1.5 · Cited by 1

TransDrift: Modeling Word-Embedding Drift using Transformer

Madaan, N., Chaudhury, P., Kumar, N., & Bedathur, S. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3651894 · Cited by 1

Construction and Analysis of Evaluation Dataset for Japanese Lexical Semantic Change Detection

Ling, Z., Aida, T., Oka, T., & Komachi, M. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.1487 · Cited by 1

LEARNING WORD RELATEDNESS OVER TIME FOR TEMPORAL RANKING

Sigmawaty, D., & Adriani, M. (2019) · Jurnal Ilmu Komputer dan Informasi · doi:10.21609/jiki.v12i2.745 · Cited by 2

A larger-scale evaluation resource of terms and their shift direction for diachronic lexical semantics

van Aggelen, A., Fokkens, A. S., Laura Hollink, A., & van Ossenbruggen, J. (2019) · DSpace repository (University of Tartu) · Cited by 1

Word Clustering for Historical Newspapers Analysis

Pivovarova, L., Marjanen, J., & Zosa, E. (2019) · doi:10.26615/978-954-452-059-5_002 · Cited by 1

Computational Analysis of French Reborrowing Process for English Loanwords

Deng, Z., Shi, W., Zhou, P., Chen, M., & Chang, K.-W. (2019) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw48858.2019.9024757 · Cited by 1

Neural-DINF: A Neural Network based Framework for Measuring Document Influence

Tan, J., Yang, C., Li, Y., Tang, S., Huang, C. Y., & Zhuang, Y. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.534 · Cited by 1

QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian

Alkhalifa, R., Tsakalidis, A., Zubiaga, A., & Liakata, M. (2020) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.7638 · Cited by 1

Transitivity of transformation matrices to bridge word vector spaces over 1000 years

Takahashi, K., & Ohshima, H. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-020-03584-5 · Cited by 1

Predicting the Age of Scientific Papers

Savov, P., Jatowt, A., & Nielek, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77961-0_58 · Cited by 3

Overcoming issues in frequency-based extraction and lexicographic lnclusion of Korean neologisms

Nam, K. (2021) · Lexicography · doi:10.1558/lexi.19481 · Cited by 1

A Research on the Evolution of Chinese Semantics Based on Distributed Representation

Wang, H., & Wang, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-81197-6_61 · Cited by 1

Opinions are Made to be Changed

Alkhalifa, R., Kochkina, E., & Zubiaga, A. (2021) · doi:10.1145/3472720.3483620 · Cited by 1

Predicting Long-Term Citations from Short-Term Linguistic Influence

Soni, S., Bamman, D., & Eisenstein, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.418 · Cited by 1

The Birth of the Foreign : A Digital Conceptual History of Buitenland in Dutch newspapers 1815-1914

Ros, R. (2019) · Utrecht University Repository (Utrecht University) · Cited by 1

Empirical Study of Diachronic Word Embeddings for Scarce DataPreprint

Montariol, S., & Allauzen, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01863 · Cited by 1

Autoencoding Word Representations through Time for Semantic Change DetectionPreprint

Tsakalidis, A., & Liakata, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13703 · Cited by 1

Shared task: Lexical semantic change detection in German (Student Project Report)Preprint

Ahmad, A., Desta, K., Lang, F., & Schlechtweg, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.07786 · Cited by 1

Unsupervised Embedding-based Detection of Lexical Semantic ChangesPreprint

Asgari, E., Ringlstetter, C., & Schütze, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07979 · Cited by 1

SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding SpacesPreprint

Vani, K., Mitrović, S., Antonucci, A., & Rinaldi, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00857 · Cited by 1

Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers

Soni, S., Klein, L. F., & Eisenstein, J. (2020) · Harvard Dataverse · doi:10.7910/dvn/fc9wm9 · Cited by 1

Detecting and modeling syntactic change

Hou, L. (2022) · doi:10.17760/d20467210 · Cited by 1

DUKweb, diachronic word representations from the UK Web Archive corpus

Tsakalidis, A., Basile, P., Bazzi, M., Cucuringu, M., & McGillivray, B. C. (2021) · Scientific Data · doi:10.1038/s41597-021-01047-x · Cited by 4

Finding "Similar" Concepts with Evidences across Different Feature Vector Spaces

Zhang, Y., Jatowt, A., & Tanaka, K. (2016)

CoVeR: Learning Covariate-Specific Vector Representations with Tensor DecompositionsPreprint

Tian, K., Zhang, T., & Zou, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.07839

A New Language Evolution Model for Chinese Spatial Preposition

Rao, Q., & Zheng, Y. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-1651-7_49

Tracing metaphors in time through self-distance in vector spacesPreprint

Tredici, M. D., Nissim, M., & Zaninello, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.03279

Short-Term Meaning Shift: A Distributional ExplorationPreprint

Tredici, M. D., Fernández, R., & Boleda, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03169

An Improved Historical Embedding without AlignmentPreprint

Xu, X., Deng, K., Hu, F., & Li, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.08692 · Cited by 2

Automatic Detection of Word Substitutions within a Language over Periods of Time

Almahdi, M. E., & Akgül, Y. S. (2019) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk.2019.8906998

The Dynamics of Semantic Change: A Corpus-Based Analysis

Boukhaled, M. A., Fagard, B., & Poibeau, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37494-5_1 · Cited by 1

Diachronic Synonymy and Polysemy: Exploring Dynamic Relation Between Forms and Meanings of Words Based on Word Embeddings

Liang, S., Zheng, J., Tang, X., Hu, R., & Liu, Z. (2019) · International Conference on Asian Language Processing · doi:10.1109/ialp48816.2019.9037663

SEDE: Semantic Evolution-based Dynamic Knowledge Graph Embedding.

Zhou, Y., Jia, P., Wang, L., Zha, D., & Mu, N. (2019)

Word Embeddings: Stability and Semantic ChangePreprint

Rettenmeier, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.16006 · Cited by 1

Semantic coordinates analysis reveals language changes in the AI fieldPreprint

Zhu, Z., Xu, Y., & Rudzicz, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.00543

Learning language variations in news corpora through differential embeddingsPreprint

Selmo, C., Martinez, J. F., Beiró, M. G., & Alvarez-Hamelin, J. I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06949

QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian (short paper).

Alkhalifa, R., Tsakalidis, A., Zubiaga, A., & Liakata, M. (2020)

Filtered Inner Product Projection for Multilingual Embedding Alignment

Sachidananda, V., Yang, Z., & Zhu, C. (2021) · International Conference on Learning Representations

Detecting Lexical Semantic Change Using Probabilistic Gaussian Word Embeddings

Moss, A. (2020) · KTH Publication Database DiVA (KTH Royal Institute of Technology)

Combating Temporal Drift in Crisis with Adapted Embeddings.Preprint

Stowe, K., & Gurevych, I. (2021) · arXiv (Cornell University)

Neural Machine Translation from Historical Japanese to Contemporary Japanese Using Diachronically Domain-Adapted Word Embeddings

Takaku, M., Hirasawa, T., Komachi, M., & Komiya, K. (2020) · Pacific Asia Conference on Language, Information, and Computation

A Statistical Model of Word Rank EvolutionPreprint

Quijano, A. J., Dale, R. A. C., & Sindi, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.09948

The Corpora They Are a-Changing: a Case Study in Italian Newspapers

Basile, P., Caputo, A., Caselli, T., Cassotti, P., & Varvara, R. (2021) · Workshop on Computational Approaches to Historical Language Change (LChange) · doi:10.18653/v1/2021.lchange-1.3

Modeling the Evolution of Word Senses with Force-Directed Layouts of Co-occurrence Networks

Reke, T., Schwanhold, R., & Krestel, R. (2021) · Workshop on Computational Approaches to Historical Language Change (LChange) · doi:10.18653/v1/2021.lchange-1.8

Tamil, Greek, Hebrew and Sanskrit: Sandalwood (Σανταλόξυλο) and its Semantics in Classical Literatures

Pugazhendhi, D. (2021) · Athens Journal of Philology · doi:10.30958/ajp.8-3-3

Embeddings-based detection of word use variation in Italian newspapers

Cafagna, M., Mattei, L. D., & Nissim, M. (2020) · Italian Journal of Computational Linguistics · doi:10.4000/ijcol.703

Monitoring geometrical properties of word embeddings for detecting the emergence of new topicsPreprint

Christophe, C., Velcin, J., Cugliari, J., Boumghar, M., & Suignard, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03496

Towards Using Diachronic Distributed Word Representations as Models of Lexical DevelopmentPreprint

Gupta, A., Patil, R., & Baths, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.05091

Delegitimizing Opposition Party after Autocratization: Evidence from South Korean NewspapersPreprint

Jang, J. (2022) · doi:10.31235/osf.io/6pk2z

Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings

Boldsen, S., & Paggio, P. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.463

Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)

(2022) · doi:10.18653/v1/2022.evonlp-1

Word Representation Learning

Hu, S., Liu, Z., Lin, Y., & Sun, M. (2023) · Springer eBooks · doi:10.1007/978-981-99-1600-9_2

On the Semantic Difference of Judicial and Standard Language

Behnke, G., & Wais, N. (2023) · International Conference on Artificial Intelligence and Law · doi:10.1145/3594536.3595123

Literary Intertextual Semantic Change Detection: Application and Motivation for Evaluating Models on Small Corpora

Ehrenworth, J., & Keith, K. A. (2023) · Workshop on Computational Approaches to Historical Language Change (LChange) · doi:10.18653/v1/2023.lchange-1.1

Does Bert Know How ‘Virus’ Evolved: Tracking Usage Changes in Chinese Textual Data

Chen, J., Qiu, L., Peng, B., & Huang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0586-3_10

Static, Dynamic, or Contextualized: What is the Best Approach for Discovering Semantic Shifts in Russian Media?

Nikonova, V., & Tikhonova, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-54534-4_10

Seeing the Shift: Keep an Eye on Semantic Changes in Times of LLMs

Buchmüller, R., Körte, F., & Keim, D. A. (2024) · IEEE Visualization in Data Science (VDS) · doi:10.1109/vds63897.2024.00010 · Cited by 1

Measuring Tone Shifts in Media Coverage: An Empirical Application to Bank Financing Conditions in ItalyPreprint

Marchetti, S., Tarducci, E., & Branzoli, N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5220056

Out-of-Dictionary Meanings Detecting Using Word-Sense Disambiguation Algorithms

Borodina, D., & Morozov, D. A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-96177-9_8

Tracking dogwhistles online and through time using distributional semantics

Boholm, M., Lindgren, E., Breitholtz, E., Cooper, R., Rettenegger, G., Rönnerstrand, B., & Sayeed, A. (2026) · Journal of Computational Social Science · doi:10.1007/s42001-026-00464-5

Koopman Spectral Dynamics for Interpretable and Predictive Semantic Drift in Language Models

Mishra, S. K., Phung, V., Vu, P., Mishra, S., Mishra, S., Bijalwan, V., & Tan, T. D. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00068

Additive Compositionality in Urban Area Embeddings Based on Human Mobility Patterns

Tamura, N., Tillayeva, N., Terashima, H., Shoji, K., Katayama, S., Urano, K., Yonezawa, T., & Kawaguchi, N. (2026) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3804448

Qualitative evaluation of word embeddings : investigating the instability in neural-based models

Pierrejean, B. (2020) · theses.fr (ABES) · doi:10.70675/a1747243zc752z4da7z86dezf358c5a1d487 · Cited by 3

Inferring and Predicting Dynamic Representations for Structured Temporal Data

Delasalles, E. (2020) · theses.fr (ABES) · doi:10.70675/9b61348az1dfdz4dbcz8691zb4335bbe9d7c

A method of mining the change of word semantic across different timescales

Zhang, M. (2017) · International Conference on Computer, Automation and Power Electronics (CAPE) · doi:10.25236/cape.2017.009

Automatic Detection of Word Substitutions within a Language over Periods of Time

Almahdi, M. E., & Akgül, Y. S. (2019) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk.2019.8906998

Times Are Changing: Investigating the Pace of Language Change in Diachronic Word Embeddings

Brandl, S., & Lassner, D. (2019) · doi:10.13140/rg.2.2.31753.19043

Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change

Giulianelli, M., Kutuzov, A., & Pivovarova, L. (2022) · Workshop on Computational Approaches to Historical Language Change (LChange) · doi:10.18653/v1/2022.lchange-1.6 · Cited by 14

Statistically Significant Detection of Linguistic ChangePreprint

Kulkarni, V., Al‐Rfou, R., Perozzi, B., & Skiena, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.3315 · Cited by 14