Distributional Structure

When do languages use the same word for different meanings? The Goldilocks principle in colexification

Brochhagen, T., & Boleda, G. (2022) · Cognition · doi:10.1016/j.cognition.2022.105179 · Cited by 27

From Text Representation to Financial Market Prediction: A Literature Review

Farimani, S. A., Jahan, M. V., & Fard, A. M. (2022) · Information · doi:10.3390/info13100466 · Cited by 32

Adaptive class augmented prototype network for few-shot relation extraction

Li, R., Zhong, J., Hu, W., Dai, Q., Wang, C., Wang, W., & Li, X. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.025 · Cited by 27

Paraphrase Identification With Deep Learning: A Review of Datasets and Methods

Zhou, C., Qiu, C. J., Liang, L., & Acuna, D. E. (2025) · IEEE Access · doi:10.1109/access.2025.3556899 · Cited by 48

A Survey of Computational Semantics: Representation, Inference and Knowledge in Wide‐Coverage Text Understanding

Bos, J. (2011) · Language and Linguistics Compass · doi:10.1111/j.1749-818x.2011.00284.x · Cited by 27

Contradiction Detection with Contradiction-Specific Word Embedding

Li, L., Qin, B., & Liu, T. (2017) · Algorithms · doi:10.3390/a10020059 · Cited by 30

Low-resource text classification using domain-adversarial learning

Grießhaber, D., Vu, N. T., & Maucher, J. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.101056 · Cited by 29

Deep recurrent convolutional networks for inferring user interests from social media

Kang, J., Choi, H., & Lee, H. (2018) · Journal of Intelligent Information Systems · doi:10.1007/s10844-018-0534-3 · Cited by 28

NEO: A Tool for Taxonomy Enrichment with New Emerging Occupations

Giabelli, A., Malandri, L., Mercorio, F., Mezzanzanica, M., & Seveso, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62466-8_35 · Cited by 27

Learning deterministic context free grammars: The Omphalos competition

Clark, A. (2006) · Machine Learning · doi:10.1007/s10994-006-9592-9 · Cited by 27

Trait Associations for Hillary Clinton and Donald Trump in News Media: A Computational Analysis

Bhatia, S., Goodwin, G. P., & Walasek, L. (2018) · Social Psychological and Personality Science · doi:10.1177/1948550617751584 · Cited by 26

Similarity is closeness: Using distributional semantic spaces to model similarity in visual and linguistic metaphors

Bolognesi, M., & Aina, L. (2017) · Corpus Linguistics and Linguistic Theory · doi:10.1515/cllt-2016-0061 · Cited by 26

Topic Extraction: BERTopic’s Insight into the 117th Congress’s Twitterverse

Mendonça, M., & Figueira, Á. (2024) · Informatics · doi:10.3390/informatics11010008 · Cited by 37

On Cyber-Social Learning: A Critique of Artificial Intelligence in Education

Cope, B., & Kalantzis, M. (2024) · Postdigital science and education · doi:10.1007/978-3-031-64487-0_1 · Cited by 31

Context encoders as a simple but powerful extension of word2vec

Horn, F. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2602 · Cited by 26

Detection and Characterization of Human Trafficking Networks Using Unsupervised Scalable Text Template Matching

Li, L., Simek, O., Lai, A., Daggett, M., Dagli, C. K., & Jones, C. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622189 · Cited by 26

Automatically detecting the scopes of source code comments

Chen, H., Huang, Y., Liu, Z., Chen, X., Zhou, F., & Luo, X. (2019) · Journal of Systems and Software · doi:10.1016/j.jss.2019.03.010 · Cited by 25

Semi-supervised distributed representations of documents for sentiment analysis

Park, S., Lee, J., & Kim, K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.08.001 · Cited by 26

Joint representation learning with ratings and reviews for recommendation

Wang, Z., Xia, H., Chen, S., & Chun, G. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.033 · Cited by 26

Word embeddings for biomedical natural language processing: A survey

Chiu, B., & Baker, S. (2020) · Language and Linguistics Compass · doi:10.1111/lnc3.12402 · Cited by 31

Network representation learning systematic review: Ancestors and current development state

Amara, A., Taieb, M. A. H., & Aouicha, M. B. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100130 · Cited by 31

Discrete and continuous representations and processing in deep learning: Looking forward

Cartuyvels, R., Spinks, G., & Moens, M. (2021) · AI Open · doi:10.1016/j.aiopen.2021.07.002 · Cited by 32

Elite Polarization in South Korea: Evidence from a Natural Language Processing Model

Han, S. (2022) · Journal of East Asian Studies · doi:10.1017/jea.2021.36 · Cited by 26

An interpretable machine learning approach for engineering change management decision support in automotive industry

Pan, Y., & Stark, R. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103633 · Cited by 34

Text Adversarial Attacks and Defenses: Issues, Taxonomy, and Perspectives

Han, X., Zhang, Y., Wang, W., & Wang, B. (2022) · Security and Communication Networks · doi:10.1155/2022/6458488 · Cited by 32

How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning

Heitmeier, M., Chuang, Y., & Baayen, R. H. (2023) · Cognitive Psychology · doi:10.1016/j.cogpsych.2023.101598 · Cited by 26

Approximate Scalable Bounded Space Sketch for Large Data NLP

Goyal, A., & Daumé, H. (2012) · Cited by 26

Semantic Aspects in Sentiment Analysis

Nissim, M., & Patti, V. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804412-4.00003-6 · Cited by 26

Smartphone user segmentation based on app usage sequence with neural networks

Lee, Y., Park, I.-B., Cho, S., & Choi, J. (2017) · Telematics and Informatics · doi:10.1016/j.tele.2017.12.007 · Cited by 26

Concept embedding to measure semantic relatedness for biomedical information ontologies

Park, J., Kim, K., Hwang, W., & Lee, D. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103182 · Cited by 26

Enhanced word embeddings using multi-semantic representation through lexical chains

Ruas, T., Ferreira, C. H. P., Grosky, W. I., de França, F. O., & de Medeiros, D. M. R. (2020) · Information Sciences · doi:10.1016/j.ins.2020.04.048 · Cited by 28

The Potential of Digital Twin Model Integrated With Artificial Intelligence Systems

Agostinelli, S., Cumo, F., Guidi, G., & Tomazzoli, C. (2020) · IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) · doi:10.1109/eeeic/icpseurope49358.2020.9160810 · Cited by 26

Sense tagging: does It make sense?

Véronis, J. (2001) · Cited by 26

Expanding Paraphrase Lexicons by Exploiting Lexical Variants

Fujita, A., & Isabelle, P. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1065 · Cited by 25

Elevating the park experience: Exploring asymmetric relationships in visitor satisfaction at Canadian national parks

Zolfaghari, A., & Choi, H. C. (2023) · Journal of Outdoor Recreation and Tourism · doi:10.1016/j.jort.2023.100666 · Cited by 26

GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models

Fang, Y., Fan, D., Zha, D., & Tan, Q. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3672035 · Cited by 29

Semi-automatic entity set refinement

Vyas, V., & Pantel, P. (2009) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1620754.1620796 · Cited by 25

Benchmarking Ontologies: Bigger or Better?

Yao, L., Divoli, A., Mayzus, I., Evans, J. A., & Rzhetsky, A. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001055 · Cited by 25

A Bayesian Method for Robust Estimation of Distributional Similarities

Kazama, J., Saeger, S. D., Kuroda, K., Murata, M., & Torisawa, K. (2010) · Cited by 25

Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection

Henriksson, A., Zhao, J., Boström, H., & Dalianis, H. (2015) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2015.7359705 · Cited by 25

Taking Antonymy Mask off in Vector Space

Santus, E., Lu, Q., Lenci, A., & Huang, C. (2014) · Institutional Repositories DataBase (IRDB) · Cited by 25

Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network

Stewart, I., Arendt, D., Bell, E., & Volkova, S. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14938 · Cited by 25

Mining Cross-Cultural Differences and Similarities in Social Media

Lin, B. Y., Xu, F. F., Zhu, K. Q., & Hwang, S.-W. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1066 · Cited by 25

A computational approach to lexical polysemy in Ancient Greek

McGillivray, B. C., Hengchen, S., Lähteenoja, V., Palma, M., & Vatri, A. (2019) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqz036 · Cited by 25

A universal cross language software similarity detector for open source software categorization

Nafi, K. W., Roy, B., Roy, C. K., & Schneider, K. A. (2019) · Journal of Systems and Software · doi:10.1016/j.jss.2019.110491 · Cited by 28

Discovering, Explaining and Summarizing Controversial Discussions in Community Q&A Sites

Ren, X., Xing, Z., Xia, X., Li, G., & Sun, J. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00024 · Cited by 25

Detection of Suicidal Intent in Spanish Language Social Networks using Machine Learning

Valeriano, K., Condori-Larico, A., & Sulla-Torres, J. A. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110489 · Cited by 29

Exploiting semantic relationships for unsupervised expansion of sentiment lexicons

Viegas, F. A. R., Alvim, M. S., Canuto, S., Rosa, T. C., Gonçalves, M. A., & da Rocha, L. C. D. (2020) · Information Systems · doi:10.1016/j.is.2020.101606 · Cited by 25

Linguistic Variation and Change in 250 Years of English Scientific Writing: A Data-Driven Approach

Bizzoni, Y., Degaetano‐Ortlieb, S., Fankhauser, P., & Teich, E. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00073 · Cited by 25

Improved biomedical word embeddings in the transformer era

Noh, J., & Kavuluru, R. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103867 · Cited by 29

LMMS reloaded: Transformer-based sense embeddings for disambiguation and beyond

Loureiro, D., Jorge, A., & Camacho-Collados, J. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103661 · Cited by 28

MIDI2vec: Learning MIDI embeddings for reliable prediction of symbolic music metadata

Lisena, P., Meroño-Peñuela, A., & Raphaël Troncy, A. (2021) · Semantic Web · doi:10.3233/sw-210446 · Cited by 25

Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies

da Silva, N. F. F., Silva, M. C. R., Pereira, F. S. F., Tarrega, J. P. M., Beinotti, J. V. P., Fonseca, M., de Andrade, F. E., & de Carvalho, A. C. P. L. F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91699-2_8 · Cited by 26

Fast and Robust Online Handwritten Chinese Character Recognition With Deep Spatial and Contextual Information Fusion Network

Li, Y., Yang, Q., Chen, Q., Hu, B., Wang, X., Ding, Y., & Ma, L. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3143324 · Cited by 27

Making it abstract, making it contestable: politicization at the intersection of political and cognitive science

Mazzuca, C., & Santarelli, M. (2022) · Review of Philosophy and Psychology · doi:10.1007/s13164-022-00640-2 · Cited by 26

Embeddings for Automatic Short Answer Grading: A Scoping Review

Putnikovic, M., & Jovanović, J. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3253071 · Cited by 32

Natural language processing for humanitarian action: Opportunities, challenges, and the path toward humanitarian NLP

Rocca, R., Tamagnone, N., Fekih, S., Contla, X., & Rekabsaz, N. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1082787 · Cited by 31

What do firms say in reporting on impacts of climate change? An approach to monitoring ESG actions and environmental policy

Chou, C., Clark, R., & Kimbrough, S. O. (2023) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.2509 · Cited by 25

Translation-oriented Word Sense Induction Based on Parallel Corpora

Apidianaki, M. (2008) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/446y3wckqb4c · Cited by 25

Towards a Historical Text Re-use Detection

Büchler, M., Burns, P. R., Müller, M., Franzini, E., & Franzini, G. (2014) · Theory and applications of natural language processing · doi:10.1007/978-3-319-12655-5_11 · Cited by 25

An Empirical Characterisation of Response Types in German Association Norms

Walde, S. S. I., Melinger, A., Roth, M., & Weber, A. (2008) · Research on Language and Computation · doi:10.1007/s11168-008-9048-4 · Cited by 25

A Rudimentary Lexicon and Semantics Help Bootstrap Phoneme Acquisition

Fourtassi, A., & Emmanuel Dupoux, A. (2014) · Conference on Computational Natural Language Learning · doi:10.3115/v1/w14-1620 · Cited by 25

Predicting audio-visual salient events based on visual, audio and text modalities for movie summarization

Koutras, P., Zlatintsi, A., Iosif, E., Katsamanis, A., Maragos, P. A., & Potamianos, A. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351630 · Cited by 25

Mining revision histories to detect cross-language clones without intermediates

Cheng, X. Y., Peng, Z., Jiang, L., Zhong, H., Yu, H., & Zhao, J. (2016) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/2970276.2970363 · Cited by 25

Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases

Härtung, M., Kaupmann, F., Jebbara, S., & Cimiano, P. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1006 · Cited by 25

Unsupervised Part-of-Speech Acquisition for Resource-Scarce Languages

Dasgupta, S., & Ng, V. (2007) · Cited by 25

VQA and Visual Reasoning: An overview of approaches, datasets, and future direction

Zakari, R. Y., Owusu, J. W., Qin, K., Wang, H., Lawal, Z. K., & He, T. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129345 · Cited by 27

Context Vector Disambiguation for Bilingual Lexicon Extraction from Comparable Corpora

Bouamor, D., Semmar, N., & Zweigenbaum, P. (2013) · Cited by 24

BOUNDARIES OF THE ONTOLOGY OF DESIGNING

Боргест, Н. М. (2017) · Ontology of Designing · doi:10.18287/2223-9537-2017-7-1-7-33 · Cited by 24

Incorporating Subword Information into Matrix Factorization Word Embeddings

Salle, A., & Villavicencio, A. (2018) · doi:10.18653/v1/w18-1209 · Cited by 24

Machine Learning Methods for Disease Prediction with Claims Data

Christensen, T., Frandsen, A., Glazier, S., Humpherys, J., & Kartchner, D. (2018) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2018.00108 · Cited by 24

Reliability-aware Dynamic Feature Composition for Name Tagging

Lin, Y., Liu, L., Ji, H., Yu, D., & Han, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1016 · Cited by 23

Improving Sentence Retrieval from Case Law for Statutory Interpretation

Šavelka, J., Xu, H., & Ashley, K. D. (2019) · International Conference on Artificial Intelligence and Law · doi:10.1145/3322640.3326736 · Cited by 24

On the Estimation of Treatment Effect with Text Covariates

Yao, L., Li, S., Li, Y., Xue, H., Gao, J., & Zhang, A. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/570 · Cited by 23

Evaluating Automatic Term Extraction Methods on Individual Documents

Šajatović, A., Buljan, M., Šnajder, J., & Bašić, B. D. (2019) · Workshop on Multiword Expressions (MWE) · doi:10.18653/v1/w19-5118 · Cited by 24

News Classification for Identifying Traffic Incident Points in a Spanish-Speaking Country: A Real-World Case Study of Class Imbalance Learning

Rivera, G., Florencia, R., García, V., Ruiz, A., & Sánchez-Solís, J. P. (2020) · Applied Sciences · doi:10.3390/app10186253 · Cited by 24

Portuguese word embeddings for the oil and gas industry: Development and evaluation

Gomes, D. D. S. M., Cordeiro, F. C., Consoli, B. S., Santos, N. L., Moreira, V. P., Vieira, R. L. R., Moraes, S., & Evsukoff, A. G. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103347 · Cited by 27

Sentence Embeddings and High-Speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents

Westermann, H., Šavelka, J., Walker, V. R., Ashley, K. D., & Benyekhlef, K. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200860 · Cited by 25

Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction

Bravo-Márquez, F., Khanchandani, A., & Pfahringer, B. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09831-y · Cited by 24

Virtual Augmentation Supported Contrastive Learning of Sentence Representations

Zhang, D., Wei, X., Zhu, H., Ma, X., & Arnold, A. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.70 · Cited by 26

A zero-shot learning fault diagnosis method of rolling bearing based on extended semantic information under unknown conditions

Yang, B., & Sun, H. (2022) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-022-03965-2 · Cited by 24

AI-powered decision-making in facilitating insurance claim dispute resolution

Zhang, W., Shi, J., Wang, X., & Wynn, H. P. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05631-9 · Cited by 34

Physics language and language use in physics—What do we know and how AI might enhance language-related research and instruction

Wulff, P. (2023) · European Journal of Physics · doi:10.1088/1361-6404/ad0f9c · Cited by 24

Human languages with greater information density have higher communication speed but lower conversation breadth

Aceves, P., & Evans, J. A. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01815-w · Cited by 24

Exploiting semantic annotations for open information extraction: an experience in the biomedical domain

Nebot, V., & Berlanga, R. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0590-x · Cited by 24

Employing topic models for pattern-based semantic class discovery

Zhang, H., Zhu, M., Shi, S., & Wen, J.-R. (2009) · doi:10.3115/1687878.1687943 · Cited by 24

Lemmatization for variation-rich languages using deep learning

Kestemont, M., Pauw, G. D., van Nie, R., & Daelemans, W. M. P. (2016) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqw034 · Cited by 34

Temporal Interaction and Causal Influence in Community-Based Question Answering

Wu, F. Q., Duan, X., Xiao, J., Zhao, Z., Tang, S., Zhang, Y., & Zhuang, Y. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720737 · Cited by 24

Corpus Specificity in LSA and Word2vec: The Role of Out-of-Domain Documents

Altszyler, E., Sigman, M., & Slezak, D. F. (2018) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w18-3001 · Cited by 24

The Role of Negative Information in Distributional Semantic Learning

Johns, B. T., Mewhort, D. J. K., & Jones, M. N. (2019) · Cognitive Science · doi:10.1111/cogs.12730 · Cited by 23

Does using a foreign language reduce mental imagery?

Montero‐Melis, G., Isaksson, P., van Paridon, J., & Ostarek, M. (2019) · Cognition · doi:10.1016/j.cognition.2019.104134 · Cited by 24

P-SIF: Document Embeddings Using Partition Averaging

Gupta, V., Saw, A., Nokhiz, P., Netrapalli, P., Rai, P., & Talukdar, P. P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6292 · Cited by 25

Interpretation of Discourse Connectives Is Probabilistic: Evidence From the Study of But and Although

Asr, F. T., & Demberg, V. (2020) · Discourse Processes · doi:10.1080/0163853x.2019.1700760 · Cited by 24

Document Clustering Evaluation: Divergence from a Random BaselinePreprint

Vries, C. M. D., Geva, S., & Trotman, A. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1208.5654 · Cited by 24

Judgment Language Matters: Multilingual Vector Space Models for Judgment Language Aware Lexical Semantics.Preprint

Leviant, I., & Reichart, R. (2015) · arXiv (Cornell University) · Cited by 24

Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddingsPreprint

Grand, G., Blank, I., Pereira, F., & Fedorenko, E. G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.01241 · Cited by 24

Social biases in word embeddings and their relation to human cognitionPreprint

Caliskan, A., & Lewis, M. L. (2020) · doi:10.31234/osf.io/d84kg · Cited by 24

Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

Augenstein, I., Gella, S., Ruder, S., Kann, K., Can, B., Conneau, A., Welbl, J., Ren, X., & Rei, M. (2019) · doi:10.18653/v1/w19-43 · Cited by 24

BLOOMFIELD'S MORPHOLOGY AND ITS SUCCESSORS1

Matthews, P. H. (1992) · Transactions of the Philological Society · doi:10.1111/j.1467-968x.1992.tb01059.x · Cited by 23

Clustering Web Search Results with Maximum Spanning Trees

Marco, A. D., & Roberto Navigli, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23954-0_20 · Cited by 23

Research in the supporting sciences

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The influence of social, cultural, and natural factors on language structure: An overview

戴智偉, & Busser, R. D. (2015) · Cited by 2

The evolution of language structures

Dunn, M. (2009)

Linguistic structure evolves to match meaning structure

Tamariz, M. (2011) · eScholarship (California Digital Library) · Cited by 4

The Nature and Development of Language

Harris, Z. S. (1991) · doi:10.1093/oso/9780198242246.003.0012