Cited by 4,807Syntax, Semantics, Linguistic VariationLinguistics and Language StudiesHistorical Linguistics and Language Studies

The DOI matches its Crossref record.

Distributional Structure

Harris, Zellig S.·1954·WORD

DOI 10.1080/00437956.1954.11659520

Open this paper in CoChat — free account

Abstract

For the purposes of the present discussion, the term structure will be used in the following non-rigorous sense: A set of phonemes or a set of data is structured in respect to some feature, to the extent that we can form in terms of that feature some organized system of statements which describes the members of the set and their interrelations (at least up to some limit of complexity). In this sense, language can be structured in respect to various independent features. And whether it is structured (to more than a trivial extent) in respect to, say, regular historical change, social intercourse, meaning, or distribution - or to what extent it is structured in any of these respects - is a matter decidable by investigation. Here we will discuss how each language can be described in terms of a distributional structure, i.e. in terms of the occurrence of parts (ultimately sounds) relative to other parts, and how this description is complete without intrusion of other features such as history or meaning. It goes without saying that other studies of language - historical, psychological, etc.-are also possible, both in relation to distributional structure and independently of it.

Integrity signals

Registry

Crossref · 10.1080/00437956.1954.11659520

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Still being checked.

Sorted by citations

Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
Distributed Representations of Sentences and DocumentsPreprint
Le, Q. V., & Mikolov, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.4053 · Cited by 7,905
LIBLINEAR: A Library for Large Linear Classification
Fan, R.-E., Chang, K., Hsieh, C., Xiang-rui, W., & Lin, C. (2008) · doi:10.5555/1390681.1442794 · Cited by 6,684
Language identification in the limit
Gold, E. M. (1967) · Information and Control · doi:10.1016/s0019-9958(67)91165-5 · Cited by 3,572
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
From Frequency to Meaning: Vector Space Models of Semantics
Turney, P. D., & Pantel, P. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2934 · Cited by 2,949
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Young, P., Lai, A., Hodosh, M., & Hockenmaier, J. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00166 · Cited by 2,439
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
Neural Word Embedding as Implicit Matrix Factorization
Levy, O., & Goldberg, Y. (2014) · neural information processing systems · Cited by 1,581
Relation Classification via Convolutional Deep Neural Network
Zeng, D., Liu, K., Lai, S., Zhou, G., & Zhao, J. (2015) · Cited by 1,467
Contextual correlates of synonymy
Rubenstein, H., & Goodenough, J. B. (1965) · Communications of the ACM · doi:10.1145/365628.365657 · Cited by 1,429
Improving Distributional Similarity with Lessons Learned from Word Embeddings
Levy, O., Goldberg, Y., & Dagan, I. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00134 · Cited by 1,344
Theories of Lexical Semantics
Geeraerts, D. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198700302.001.0001 · Cited by 1,265
code2vec: Learning distributed representations of code
Alon, U., Zilberstein, M., Levy, O., & Yahav, E. (2019) · Proceedings of the ACM on Programming Languages · doi:10.1145/3290353 · Cited by 962
Machine Learning Methods That Economists Should Know About
Athey, S., & Imbens, G. W. (2019) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053433 · Cited by 1,163
Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]
Cambria, E., & White, B. (2014) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2014.2307227 · Cited by 1,399
Dependency-Based Word Embeddings
Levy, O., & Goldberg, Y. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2050 · Cited by 1,125
Measuring Corporate Culture Using Machine Learning
Li, K., Mai, F., Shen, R., & Yan, X. (2020) · Review of Financial Studies · doi:10.1093/rfs/hhaa079 · Cited by 1,022
Word2Vec
Church, K. (2016) · Natural Language Engineering · doi:10.1017/s1351324916000334 · Cited by 1,029
Composition in Distributional Models of Semantics
Mitchell, J., & Lapata, M. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2010.01106.x · Cited by 968
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1141 · Cited by 1,170
Multimodal Distributional Semantics
Bruni, E., Tran, N. K., & Baroni, M. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4135 · Cited by 929
Word Translation Without Parallel DataPreprint
Conneau, A., Lample, G., Ranzato, M., Denoyer, L., & Jeǵou, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.04087 · Cited by 1,105
How to do Linguistics with R
Levshina, N. (2015) · John Benjamins Publishing Company eBooks · doi:10.1075/z.195 · Cited by 862
Evaluation of output embeddings for fine-grained image classification
Akata, Z., Reed, S., Walter, D., Lee, H., & Schiele, B. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298911 · Cited by 1,022
Neural Network Methods for Natural Language Processing
Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821
Automatic text summarization: A comprehensive survey
El-Kassas, W. S., Salama, C. R., Rafea, A., & Mohamed, H. K. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113679 · Cited by 906
Topic Modeling in Embedding Spaces
Dieng, A. B., Ruiz, F. J. R., & Blei, D. M. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00325 · Cited by 906
Learning Deep Representations of Fine-Grained Visual Descriptions
Reed, S., Akata, Z., Lee, H., & Schiele, B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.13 · Cited by 818
Second Language Acquisition
Ritchie, W. C., & Bhatia, T. K. (1996) · Elsevier eBooks · doi:10.1016/b978-012589042-7/50003-7 · Cited by 747
Multiple instance learning: A survey of problem characteristics and applications
Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.009 · Cited by 740
Matching the Blanks: Distributional Similarity for Relation Learning
Baldini Soares, L., FitzGerald, N., Ling, J., & Kwiatkowski, T. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1279 · Cited by 731
Applications of deep learning in stock market prediction: Recent progress
Jiang, W. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115537 · Cited by 809
An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools
Lauriola, I., Lavelli, A., & Aiolli, F. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.103 · Cited by 755
Poincaré Embeddings for Learning Hierarchical Representations
Nickel, M., & Kiela, D. (2017) · arXiv · doi:10.48550/arxiv.1705.08039 · Cited by 667
Introduction to the special issue on word sense disambiguation: the state of the art
Ide, N., & Véronis, J. (1998) · Cited by 666
Distributional Memory: A General Framework for Corpus-Based Semantics
Baroni, M., & Lenci, A. (2010) · Computational Linguistics · doi:10.1162/coli_a_00016 · Cited by 660
Improving Vector Space Word Representations Using Multilingual Correlation
Faruqui, M., & Dyer, C. (2014) · Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1049 · Cited by 620
Discovering word senses from text
Pantel, P., & Lin, D. (2002) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775138 · Cited by 604
Emotional behavior in long-term marriage.
Carstensen, L. L., Gottman, J. M., & LEVENSON, R. W. (1995) · Psychology and Aging · doi:10.1037//0882-7974.10.1.140 · Cited by 579
Machine learning and natural language processing in mental health: systematic review
Le Glaz, A., Haralambous, Y., Kim-Dufor, D.-H., Lenca, P., Billot, R., Ryan, T. C., Marsh, J., DeVylder, J., Walter, M., Berrouiguet, S., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/15708 · Cited by 576
Evaluation of Output Embeddings for Fine-grained Image Classification
Akata, Z., Reed, S., Walter, D., Lee, H., & Schiele, B. (2015) · Max Planck Digital Library · Cited by 567
Learning Distributed Representations of Sentences from Unlabelled Data
Hill, F., Cho, K., & Korhonen, A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1162 · Cited by 566
The structure of language : Readings in the philosophy of language. Edited by Jerry A. Fodor and Jerrold J. Katz Pp. xii, 612. Englewood Cliffs, N. J.: Prentice-Hall, Inc., 1964.
Bar‐Hillel, Y. (1967) · Language · doi:10.2307/411551 · Cited by 558
Formal models of language learning
Pinker, S. A. (1979) · Cognition · doi:10.1016/0010-0277(79)90001-5 · Cited by 551
DIRT @SBT@discovery of inference rules from text
Lin, D., & Pantel, P. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502559 · Cited by 542
Discovery of inference rules for question-answering
Lin, D., & Pantel, P. (2001) · Natural Language Engineering · doi:10.1017/s1351324901002765 · Cited by 528
Learning words from sights and sounds: a computational model
Roy, D. K., & Pentland, A. (2002) · Cognitive Science · doi:10.1016/s0364-0213(01)00061-1 · Cited by 521
Distributional Information: A Powerful Cue for Acquiring Syntactic Categories
Redington, M., Chater, N., & Finch, S. (1998) · Cognitive Science · doi:10.1207/s15516709cog2204_2 · Cited by 535
Distributional regularity and phonotactic constraints are useful for segmentation
Brent, M. R., & Cartwright, T. A. (1996) · Cognition · doi:10.1016/s0010-0277(96)00719-6 · Cited by 514
Microsoft Academic Graph: When experts are not enough
Wang, K., Shen, Z., Huang, C., Wu, C., Dong, Y., & Kanakia, A. (2020) · Quantitative Science Studies · doi:10.1162/qss_a_00021 · Cited by 554
Identifying Customer Needs from User-Generated Content
Timoshenko, A., & Hauser, J. R. (2019) · Marketing Science · doi:10.1287/mksc.2018.1123 · Cited by 513
Deep Cross-Modal Projection Learning for Image-Text Matching
Zhang, Y., & Lu, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_42 · Cited by 520
Learning the protein language: Evolution, structure, and function
Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483
Extracting opinions, opinion holders, and topics expressed in online news media text
Kim, S.-M., & Hovy, E. H. (2006) · doi:10.3115/1654641.1654642 · Cited by 460
Integrating experiential and distributional data to learn semantic representations.
Andrews, M. W., Vigliocco, G., & Vinson, D. (2009) · Psychological Review · doi:10.1037/a0016261 · Cited by 477
Enriching Word Vectors with Subword InformationPreprint
Bojanowski, P., Grave, É., Joulin, A., & Mikolov, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.04606 · Cited by 457
Jumping NLP Curves: A Review of Natural Language Processing Research
Cambria, E., & White, B. (2014) · Cited by 455
Distributional Models of Word Meaning
Lenci, A. (2017) · Annual Review of Linguistics · doi:10.1146/annurev-linguistics-030514-125254 · Cited by 485
A Survey on Data Augmentation for Text Classification
Bayer, M., Kaufhold, M., & Reuter, C. (2022) · ACM Computing Surveys · doi:10.1145/3544558 · Cited by 496
A Bayesian framework for word segmentation: Exploring the effects of context
Goldwater, S. J., Griffiths, T. L., & Johnson, M. (2009) · Cognition · doi:10.1016/j.cognition.2009.03.008 · Cited by 446
Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting: A review and empirical validation
Mandera, P., Keuleers, E., & Brysbaert, M. (2016) · Journal of Memory and Language · doi:10.1016/j.jml.2016.04.001 · Cited by 460
Learning word embeddings efficiently with noise-contrastive estimation
Mnih, A., & Kavukcuoglu, K. (2013) · neural information processing systems · Cited by 447
Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation
Syed, S., & Spruit, M. R. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.61 · Cited by 453
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280
Overview of the TAC 2010 knowledge base population track
Ji, H., Grishman, R., Dang, H. T., Griffitt, K., & Ellis, J. (2010) · Cited by 404
Toward a theory of semantic representation
Vigliocco, G., Meteyard, L., Andrews, M. W., & Kousta, S. T. (2009) · Language and Cognition · doi:10.1515/langcog.2009.011 · Cited by 422
VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations
Chklovski, T. A., & Pantel, P. (2004) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · Cited by 391
A Survey of Paraphrasing and Textual Entailment Methods
Androutsopoulos, I., & Malakasiotis, P. (2010) · Cited by 386
Language in Relation to a Unified Theory of the Structure of Human Behavior
Pike, K. L. (1967) · De Gruyter eBooks · doi:10.1515/9783111657158 · Cited by 2,479
From Word To Sense Embeddings: A Survey on Vector Representations of Meaning
Camacho-Collados, J., & Pilehvar, M. T. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11259 · Cited by 393
Learning the language of viral evolution and escape
Hie, B., Zhong, E. D., Berger, B., & Bryson, B. (2021) · Science · doi:10.1126/science.abd7331 · Cited by 361
Using WordNet Based Context Vectors to Estimate the Semantic Relatedness of Concepts
Patwardhan, S., & Ted Pedersen, A. (2006) · Cited by 353
An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery
Brent, M. (1999) · Machine-mediated learning · doi:10.1023/a:1007541817488 · Cited by 336
Multi-Prototype Vector-Space Models of Word Meaning
Reisinger, J., & Mooney, R. J. (2010) · Cited by 347
Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers
Mishev, K., Gjorgjevikj, A., Vodenska, I., Chitkushev, L. T., & Trajanov, D. (2020) · IEEE Access · doi:10.1109/access.2020.3009626 · Cited by 380
Unsupervised learning of natural languages
Solan, Z., Horn, D., Ruppin, E., & Edelman, S. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409746102 · Cited by 345
Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions
Günther, F., Rinaldi, L., & Marelli, M. (2019) · Perspectives on Psychological Science · doi:10.1177/1745691619861372 · Cited by 354
Sentiment Embeddings with Applications to Sentiment Analysis
Tang, D., Wei, F., Qin, B., Yang, N., Liu, T., & Zhou, M. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2489653 · Cited by 344
How to Generate a Good Word Embedding
Lai, S., Liu, K., He, S., & Zhao, J. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.45 · Cited by 347
Gaussian LDA for Topic Models with Word Embeddings
Das, R., Zaheer, M., & Dyer, C. (2015) · doi:10.3115/v1/p15-1077 · Cited by 341
Vector Space Models of Word Meaning and Phrase Meaning: A Survey
Erk, K. (2012) · Language and Linguistics Compass · doi:10.1002/lnco.362 · Cited by 329
Topic Modeling as a Strategy of Inquiry in Organizational Research: A Tutorial With an Application Example on Organizational Culture
Schmiedel, T., Müller, O., & vom Brocke, J. (2019) · Organizational Research Methods · doi:10.1177/1094428118773858 · Cited by 319
Learning words from sights and sounds: A computational model
Roy, D. K., & Pentland, A. P. (2002) · Cognitive Science · doi:10.1207/s15516709cog2601_4 · Cited by 521
Semantic memory: A review of methods, models, and current challenges
Kumar, A. A. (2020) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01792-x · Cited by 350
Co-Occurrent Features in Semantic Segmentation
Zhang, H., Zhang, H., Wang, C., & Xie, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00064 · Cited by 302
Contextual Dependencies in Unsupervised Word Segmentation
Goldwater, S., Griffiths, T., & Johnson, M. (2006) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/1220175.1220260 · Cited by 336
Topic modeling algorithms and applications: A survey
Abdelrazek, A., Eid, Y., Gawish, E. K., Medhat, W., & Hassan, A. (2022) · Information Systems · doi:10.1016/j.is.2022.102131 · Cited by 358
Pretrained Transformers Improve Out-of-Distribution Robustness
Hendrycks, D., Liu, X., Wallace, E., Dziedzic, A., Krishnan, R., & Song, D. X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.244 · Cited by 406
Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods
Madnani, N., & Dorr, B. J. (2010) · Computational Linguistics · doi:10.1162/coli_a_00002 · Cited by 299
An Introduction to Neural Information Retrieval
Mitra, B., & Craswell, N. (2018) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000061 · Cited by 317
A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
Patil, R., & Gudivada, V. N. (2024) · Applied Sciences · doi:10.3390/app14052074 · Cited by 355
Beyond Binary Labels: Political Ideology Prediction of Twitter Users
Preoțiuc-Pietro, D., Liu, Y., Hopkins, D. J., & Ungar, L. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1068 · Cited by 310
Open Problems in Mechanistic Interpretability
Sharkey, L., Chughtai, B., Batson, J., Lindsey, J., Wu, J., Bushnaq, L., Goldowsky-Dill, N., Heimersheim, S., Ortega, A., Bloom, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.16496 · Cited by 291
From word embeddings to document similarities for improved information retrieval in software engineering
Ye, X., Shen, H., Ma, X., Bunescu, R., & Liu, C. (2016) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2884781.2884862 · Cited by 302
Vector Space Models of Lexical Meaning
Clark, S. C. (2015) · Wiley eBooks · doi:10.1002/9781118882139.ch16 · Cited by 296
Word Sense Disambiguation: The State of the Art
Ide, N., & Véronis, J. (2005) · Cited by 297
Experience Grounds Language
Bisk, Y., Holtzman, A., Thomason, J., Andreas, J., Bengio, Y., Chai, J. Y., Lapata, M., Lazaridou, A., May, J., Nisnevich, A., et al. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.703 · Cited by 421
Identity-Aware Textual-Visual Matching with Latent Co-attention
Li, S., Xiao, T., Li, H., Yang, W. P., & Wang, X. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.209 · Cited by 291

Authors: Harris, Z. S.
Corresponding author: Harris, Zellig S.
Venue: WORD
Publisher: Informa UK Limited
Published: 1954-08
Volume: 10
Issue: 2-3
Pages: 146-162
ISSN: 0043-7956 (ISSN-L)
2373-5112
DOI: 10.1080/00437956.1954.11659520
OpenAlex: W2882319491
Cited by: 4,807
References: 0
Citations by year: 2026: 181 · 2025: 217 · 2024: 303 · 2023: 371 · 2022: 365 · 2021: 428 · 2020: 448 · 2019: 474 · 2018: 373 · 2017: 324 · 2016: 279 · 2015: 242 · 2014: 175 · 2013: 159 · 2012: 92
FWCI: 16.54
Type: journal article
Language: en
Open access: closed
Locations: WORD (publishedVersion)
Topics: Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
Social Sciences › Arts and Humanities › Language and Linguistics › Linguistics and Language Studies
Social Sciences › Arts and Humanities › Language and Linguistics › Historical Linguistics and Language Studies
Keywords: phonemes, phonological features, phonological description, sound distribution, language structure, historical change, meaning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Distributional Structure

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks