Linguistic Typology

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Linguistic typology compares languages across features such as sound systems, word order, morphology, and meaning to map diversity and identify recurring patterns. Researchers use comparative databases and grammars, statistical and information-theoretic models, and cognitive or historical explanations to study how these patterns arise through learning, communication, change, and contact. Computational work also uses multilingual language models to predict linguistic features and test learnability and cross-lingual transfer. A central challenge is distinguishing genuine cognitive or structural constraints from effects of language history, contact, uneven data, and model training.

Latest papers

26 papers4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Inductive Learning of Concept Embeddings from Cross-Linguistic Colexification Patterns with Textual Attributes
Oct 15, 2026Arne RubehnRepresentation Learning Linguistic Typology
Chair for Multilingual Computational Linguistics, University of Passau, Passau, Germany
Cognitive Thermometers: Machine Learning and Logical Complexity
Oct 7, 2026Shane Steinert-Threlkeld, Jakub SzymanikLinguistic Typology Computational Cognitive Modeling
University of Washington, Department of Linguistics · University of Trento, Center for Mind/Brain Sciences and Department of Information Engineering and Computer Science
Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languages
Oct 1, 2026Nadine El-Naggar, Tatsuki Kuribayashi, Ted BriscoeSyntactic Generalization in Language Models Language Modeling
Mohamed bin Zayed University of Artificial Intelligence · Tohoku University
Zero-Compute Cross-Lingual Transferability Estimation Using Typological Feature Proxies
Sep 30, 2026Dalton Raphael Harmsen, Swier Garst, Thomas van Osch +2Task Transferability Estimation Linguistic Typology
AMOR/e Lab, Eindhoven University of Technology, Eindhoven, The Netherlands · SURF, Amsterdam, The Netherlands
LLM Agents as Computational Typologists
Sep 7, 2026Changbing Yang, Christopher Hammerly, Freda Shi +1LLM Agents Linguistic Typology
University of British Columbia · University of Waterloo · Vector Institute
Typological Feature Prediction with Large Language Models: An In-Context Learning Approach
Sep 3, 2026Qianwen Wang, York Hay Ng, Aditya Khan +1In-Context Learning Linguistic Typology
University of Toronto, Canada · Columbia · Ontario Tech University, Canada
Causal Interventions Reveal Typologically Organized Syntactic Mechanisms in Multilingual Language Models
Aug 28, 2026Sasha Boguraev, Toshiki Nakai, Kyle Mahowald +1Multilingual Language Models Linguistic Typology
The University of Texas at Austin · Leipzig University, ScaDS.AI Dresden/Leipzig · Heidelberg Institute for Theoretical Studies
Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models
Aug 15, 2026Varvara Arzt, Allan Hanbury, Terra BlevinsMultilingual Language Models Decoder-Only Language Models
Faculty of Informatics, TU Wien · Khoury College of Computer Sciences, Northeastern University
Instability of LLM Pre-Pretraining: It Doesn't Always Help. An Investigation on Multiple Languages
Aug 9, 2026Sofiia Riazhskykh, Nam Luu, Ondřej BojarLanguage Model Pretraining Linguistic Typology
Charles University, Faculty of Mathematics and Physics
Toward a systematic method for identifying language areas
Jul 28, 2026Hiram RingClustering Linguistic Typology
NTU Singapore
The Linguistics Olympiads: Towards a New Corpus for Linguistics Research?
Jun 12, 2026Vlad A. NeacsuCorpus Linguistics Linguistic Typology
“Iorgu Iordan – Al. Rosetti” Institute of Linguistics, Romanian Academy
Brain-LLM Alignment Tracks Training Data, Not Typology
May 21, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuMultilingual Language Models Cross-Lingual Representation Alignment
The University of Hong Kong Hong Kong, China · Stellaris AI Limited Hong Kong, China
What Kind of Language is Easy to Language-Model Under Curriculum Learning?
Apr 29, 2026Nadine El-Naggar, Tatsuki Kuribayashi, Ted BriscoeInductive Bias Linguistic Bias in Language Models
Mohamed bin Zayed University of Artificial Intelligence
Swap distance minimization shapes the order of subject, object and verb in languages of the world
Apr 29, 2026Jairo Rios-El-Yazidi, Ramon Ferrer-i-CanchoLinguistic Typology
Quantitative, Mathematical and Computational Linguistics Research Group, Department of Computer Science, Universitat Polit`ecnica de Catalunya, 08034 Barcelona, Catalonia (Spain)
Phonological distances for linguistic typology and the origin of Indo-European languages
Apr 13, 2026Marius Mavridis, Juan De Gregorio, Raul Toral +1Linguistic Typology Phylogenetic Inference
Institute for Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain · Universit´e Paris-Saclay, ENS Paris-Saclay, DER de Physique, 91190, Gif-sur-Yvette, France
A Modular Architecture for Typologically Controlled Lexicon Generation
Apr 7, 2026Sankalp Tattwadarshi Swain, Dhruv KumarLinguistic Typology
Birla Institute of Technology and Science, Pilani Pilani, Rajasthan 333031, India
Towards a theory of morphology-driven marking in the lexicon: The case of the state
Apr 3, 2026Mohamed El IdrissiMorphological Analysis Linguistic Typology
INALCO
The Degree of Language Diacriticity and Its Effect on Tasks
Mar 29, 2026Adi Cohen, Yuval PinterLinguistic Typology
Institute for Applied AI Research Ben-Gurion University of the Negev Beer Sheva, Israel
Vocabulary shapes cross-lingual variation of word-order learnability in language models
Mar 19, 2026Jonas Mayer Martins, Jaap Jumelet, Viola Priesemann +1Linguistic Typology
University of Göttingen, Germany · University of Groningen, Netherlands · MPI for Dynamics and Self-Organization, Germany
Modelling the Diachronic Emergence of Phoneme Frequency Distributions
Mar 10, 2026Fermín Moscoso del Prado Martín, Suchir SalhanLinguistic Typology Heavy-Tailed Distributions
Department of Computer Science and Technology, University of Cambridge, UK
Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster
Mar 7, 2026Minu Kim, Hoirin Kim, David R. MortensenSelf-Supervised Speech Representation Learning Language Identification
Thomas Lord Department of Computer Science, University of Southern California, USA · School of Electrical Engineering, KAIST, Republic of Korea · Language Technologies Institute, Carnegie Mellon University, USA
The Distribution of Phoneme Frequencies across the World's Languages: Macroscopic and Microscopic Information-Theoretic Models
Mar 3, 2026Fermín Moscoso del Prado Martín, Suchir SalhanLinguistic Typology
Department of Computer Science and Technology, University of Cambridge, UK
Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning
Feb 25, 2026Andrea Silvi, Ponrawee Prasertsom, Jennifer Culbertson +3Linguistic Typology
Chalmers University of Technology and Gothenburg University · University of Edinburgh
How communicatively optimal are exact numeral systems? Once more on lexicon size and morphosyntactic complexity
Feb 23, 2026Chundra Cathcart, Arne Rubehn, Katja Bocklage +7Linguistic Typology
Institute for the Interdisciplinary Study of Language Evolution University of Zurich, Zurich, Switzerland · Chair for Multilingual Computational Linguistics University of Passau, Passau, Germany
Computational Typology
Apr 22, 2025Gerhard JägerLinguistic Typology
University of Tübingen, Seminar für Sprachwissenschaft, Keplerstr. 2, 72074 Tübingen, Germany
Universal Topological Regularity of Syntactic Structures
Jan 31, 2023Fermín Moscoso del Prado MartínLinguistic Typology
Department of Language and Communication & Center for Language Studies Radboud University Erasmuslaan 1, 6525 NL, Nijmegen, The Netherlands

Oct 15, 2026J/K move · Enter open · S save

Inductive Learning of Concept Embeddings from Cross-Linguistic Colexification Patterns with Textual Attributes

Arne Rubehn

Chair for Multilingual Computational Linguistics, University of Passau, Passau, Germany

Representation Learning Linguistic Typology

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The advent of distributional semantics as a framework for computational semantics marked a paradigm shift in the nature of representations, by which meanings are represented as dense vectors commonly known as word embeddings. Computational approaches to historical or typological language comparison, on the other hand, still mostly rely on discrete representations, while the potential of embedding-based methods remains largely untapped in this context. Recent studies have proposed concept embeddings as a language-independent, onomasiological counterpart to word embeddings, based on networks of cross-linguistic colexification patterns. While the resulting concept embeddings reflect meaningful semantic properties and relations, they suffer from a lack of scalability, since they rely on transductive embedding approaches which only learn embeddings for nodes present in the training data. In this study, we present an inductive framework for learning concept embeddings, combining the structural properties of full and partial colexification networks with semantic information from short concept descriptions. This framework allows the embedding of unseen concepts based on a short definition. We show that the resulting concept embeddings correlate with various cross-linguistic measures of similarity or relatedness, while generalizing well to new concepts that were absent from the training data.