The DOI matches its Crossref record.
Distributional Structure
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“In LLMs, semantic similarity is determined based on distribution across contexts: words that appear in similar contexts are mapped to nearby vectors.”
From The Vector Grounding Problem (2026) · cites this paper as Harris, 1954
In the citing paper
“There is a deep connection between the way in which LLMs learn embeddings from linguistic corpora, and the old idea from structural linguistics according to which word meaning can be inferred from the contexts in which they appear, known as the distributional hypothesis (Firth, 1957;Harris, 1954).”
From The Vector Grounding Problem (2026) · cites this paper as Harris, 1954
In the citing paper
“This result mirrors a relentless trend in DL for various domains, where learned features ultimately outperform and displace long-used expert features, such as the scale-invariant feature transform (SIFT) [8] and Bag-of-words [9].”
From An Introduction to Deep Learning for the Physical Layer (2017) · cites this paper as [9]
Citations
Cites
Still being checked.
Cited by
Sorted by citations
Items 801–900 of 1,015
Structures and distributions in morphology learning
Yang, C. D., & Chan, E. (2008) · Cited by 32
Nasiruddin, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.1425 · Cited by 32
Remarks on the origins of morphophonemics in American structuralist linguistics
Koerner, E. F. K. (2002) · Language & Communication · doi:10.1016/s0271-5309(02)00024-1 · Cited by 31
Polysemy—Evidence from Linguistics, Behavioral Science, and Contextualized Language Models
Haber, J., & Poesio, M. (2023) · Computational Linguistics · doi:10.1162/coli_a_00500 · Cited by 57
Santis, E. D., Martino, A., & Rizzi, A. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3358168 · Cited by 38
Rudolph, M. (2018) · Cambridge University Engineering Department Publications Database · doi:10.7916/d8nz9rht · Cited by 33
Günther, F., Dudschig, C., & Kaup, B. (2017) · Cognitive Science · doi:10.1111/cogs.12549 · Cited by 31
Simpson, S., Adams, N., Brugman, C., & Conners, T. J. (2018) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.7726 · Cited by 31
A Systematic Study of Leveraging Subword Information for Learning Word Representations
Zhu, Y., Vulić, I., & Korhonen, A. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1097 · Cited by 37
A comparison of features extraction methods for Arabic sentiment analysis
Kasri, M., Birjali, M., & Beni‐Hssane, A. (2019) · International Conference on Big Data and Internet of Things (BDIOT) · doi:10.1145/3372938.3372998 · Cited by 31
Yang, W., & Lam, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41579-2_9 · Cited by 35
Methods for Numeracy-Preserving Word Embeddings
Sundararaman, D., Si, S., Subramanian, V., Wang, G., Hazarika, D., & Carin, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.384 · Cited by 41
The Emergence of Richly Organized Semantic Knowledge from Simple Statistics: A Synthetic Review
Unger, L., & Fisher, A. V. (2021) · Developmental Review · doi:10.1016/j.dr.2021.100949 · Cited by 31
Bi-GRU Urgent Classification for MOOC Discussion Forums Based on BERT
Khodeir, N. (2021) · IEEE Access · doi:10.1109/access.2021.3072734 · Cited by 37
A Custom Word Embedding Model for Clustering of Maintenance Records
Bhardwaj, A. S., Deep, A., Veeramani, D., & Zhou, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3079521 · Cited by 31
Rieder, B. (2020) · Amsterdam University Press eBooks · doi:10.1515/9789048537419 · Cited by 32
The Humanities in the Digital: Beyond Critical Digital Humanities
Viola, L. (2023) · Springer eBooks · doi:10.1007/978-3-031-16950-2 · Cited by 33
Syntactic dependencies correspond to word pairs with high mutual information
Futrell, R., Qian, P., Gibson, E. A. F., Fedorenko, E. G., & Blank, I. (2019) · International Conference on Dependency Linguistics (Depling) · doi:10.18653/v1/w19-7703 · Cited by 31
Combining word sense and usage for modeling frame semantics
Cao, D. D., Croce, D., Marco Pennacchiotti, B., & Basili, R. (2008) · Conference on Semantics in Text Processing (STEP) · doi:10.3115/1626481.1626489 · Cited by 31
Learning Semantic Sentence Embeddings using Sequential Pair-wise DiscriminatorPreprint
Patro, B. N., Kurmi, V. K., Kumar, S., & Namboodiri, V. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00807 · Cited by 39
Does Finance Benefit Society? A Language Embedding Approach
Jha, M., Liu, H., & Manela, A. (2025) · Review of Financial Studies · doi:10.1093/rfs/hhaf012 · Cited by 35
Semi-compositional Method for Synonym Extraction of Multi-Word Terms
Hazem, A., & Daille, B. (2014) · HAL · Cited by 30
Developing Linguistic Theories Using Annotated Corpora
de Marneffe, M.-C., & Potts, C. (2017) · Springer eBooks · doi:10.1007/978-94-024-0881-2_16 · Cited by 30
Stoltz, D. S., & Taylor, M. A. (2023) · doi:10.1093/oso/9780197756874.001.0001 · Cited by 31
Qiu, Q., Tian, M., Tao, L., Xie, Z., & Ma, K. (2024) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2023.105863 · Cited by 30
Chen, Y.-N., Wang, W. Y., & Rudnicky, A. I. (2014) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2014.7078639 · Cited by 30
Günther, F., & Marelli, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0163200 · Cited by 30
Sentence level matrix representation for document spectral clustering
Mijangos, V., Sierra, G., & Montes, A. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.11.008 · Cited by 30
Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching
Wick, M., Kanani, P. H., & Pocock, A. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10360 · Cited by 30
ONTOLOGY ENGINEERING TO SUPPORT STRATEGIC DECISIONMAKING IN THE ENERGY SECTOR
Massel, L., Николаевна, В. Т., & Pjatkova, N. (2017) · Ontology of Designing · doi:10.18287/2223-9537-2017-7-1-66-76 · Cited by 30
McEnery, T., Březina, V., & Baker, H. (2019) · International Journal of Corpus Linguistics · doi:10.1075/ijcl.18096.mce · Cited by 35
An evaluation of recent neural sequence tagging models in Turkish named entity recognition
Aras, G., Makaroğlu, D., Demir, Ş., & CAKIR, A. M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115049 · Cited by 31
Capitalization and punctuation restoration: a survey
Păiș, V., & Tufis, D. I. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10051-x · Cited by 33
Improving cross-platform binary analysis using representation learning via graph alignment
Kim, G., Hong, S., Franz, M., & Song, D. (2022) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3533767.3534383 · Cited by 30
Form to meaning mapping and the impact of explicit morpheme combination in novel word processing
Bonandrini, R., Amenta, S., Sulpizio, S., Tettamanti, M., Mazzucchelli, A., & Marelli, M. (2023) · Cognitive Psychology · doi:10.1016/j.cogpsych.2023.101594 · Cited by 31
Synthesizing high utility suggestions for rare web search queries
Jain, A., Özertem, U., & Velipasaoglu, E. (2011) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2009916.2010024 · Cited by 30
Lin, D. (2001) · International Conference on Human Language Technology Research · doi:10.3115/1072133.1072198 · Cited by 30
UCCA: A Semantics-based Grammatical Annotation Scheme
Abend, O., & Rappoport, A. (2013) · Cited by 30
Clustering for Simultaneous Extraction of Aspects and Features from Reviews
Chen, L., Martineau, J., Cheng, D. Y., & Sheth, A. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1093 · Cited by 30
Ensembles of randomized trees using diverse distributed representations of clinical events
Henriksson, A., Zhao, J., Dalianis, H., & Boström, H. (2016) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0309-0 · Cited by 30
Characters or Morphemes: How to Represent Words?
Üstün, A. B., Kurfalı, M., & Can, B. (2018) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w18-3019 · Cited by 32
Incorporating Both Distributional and Relational Semantics in Word RepresentationsPreprint
Fried, D., & Duh, K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.4369 · Cited by 30
Opportunities for large language models and discourse in engineering design
Göpfert, J., Weinand, J. M., Kuckertz, P., & Stolten, D. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100383 · Cited by 52
Practical Evaluation of ChatGPT Performance for Radiology Report Generation
Soleimani, M., Seyyedi, N., Ayyoubzadeh, S. M., Kalhori, S. R. N., & Keshavarz, H. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.07.020 · Cited by 33
Corpus-based semantic lexicon induction with Web-based corroboration
Igo, S., & Riloff, E. (2009) · doi:10.3115/1641968.1641971 · Cited by 29
Learning from syntax generalizations for automatic semantic annotation
Guido Boella, A., Di, L., Ruggeri, A., & Robaldo, L. (2014) · Journal of Intelligent Information Systems · doi:10.1007/s10844-014-0320-9 · Cited by 29
Phrase similarity in humans and machines.
Gershman, S. J., & Tenenbaum, J. B. (2015) · Cited by 30
Zhuang, H., Wang, C., Li, C., Wang, Q., & Zhou, X. (2017) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2017.46 · Cited by 29
Fast and modular regularized topic modelling
Kochedykov, D., Apishev, M., Golitsyn, L., & Vorontsov, K. V. (2017) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct.2017.8250181 · Cited by 29
Günther, F., Petilli, M. A., & Marelli, M. (2020) · Journal of Memory and Language · doi:10.1016/j.jml.2020.104104 · Cited by 33
Semantic change computation: A successive approach
Tang, X., Qu, W., & Chen, X. (2015) · World Wide Web · doi:10.1007/s11280-014-0316-y · Cited by 29
Machine Learning Techniques for the Detection of Inappropriate Erotic Content in Text
Barrientos, G. M., Aláiz-Rodríguez, R., González‐Castro, V., & Parnell, A. C. (2020) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.200519.003 · Cited by 29
Agosti, M., Marchesin, S., & Silvello, G. (2020) · ACM Transactions on Information Systems · doi:10.1145/3417996 · Cited by 30
Zhou, S., Chen, B., Zhang, Y., Liu, H., Xiao, Y., & Pan, X. (2020) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2020.11.006 · Cited by 32
Network representation learning: A macro and micro view
Liu, X., & Tang, J. (2021) · AI Open · doi:10.1016/j.aiopen.2021.02.001 · Cited by 29
Lukauskas, M., Šarkauskaitė, V., Pilinkienė, V., Stundžienė, A., Grybauskas, A., & Bruneckienė, J. (2023) · Applied Sciences · doi:10.3390/app13106119 · Cited by 42
Automatic Acquisition and Efficient Representation of Syntactic Structures
Solan, Z., Ruppin, E., Horn, D. G., & Edelman, S. (2002) · Cited by 29
Deep Learning and Its Applications to Natural Language Processing
Yang, H., Luo, L., Chueng, L. P., Ling, D., & Chin, F. Y. L. (2019) · Cognitive computation trends · doi:10.1007/978-3-030-06073-2_4 · Cited by 29
Semantic Source Code Models Using Identifier Embeddings
Efstathiou, V., & Spinellis, D. D. (2019) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2019.00015 · Cited by 28
A (distributional) semantic perspective on the processing of morphologically complex words
Amenta, S., Günther, F., & Marelli, M. (2020) · The Mental Lexicon · doi:10.1075/ml.00014.ame · Cited by 28
Deep learning models to study sentence comprehension in the human brain
Arana, S., Lerousseau, J. P., & Peter Hagoort, A. (2023) · Language Cognition and Neuroscience · doi:10.1080/23273798.2023.2198245 · Cited by 31
Grout, R., Gupta, R. A., Bryant, R., Elmahgoub, M. A., Li, Y., Irfanullah, K., Patel, R. F., Fawkes, J., & Inness, C. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1287541 · Cited by 37
Collective predictive coding hypothesis: symbol emergence as decentralized Bayesian inference
Taniguchi, T. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1353870 · Cited by 28
Inside Out: Two Jointly Predictive Models for Word Representations and Phrase Representations
Sun, F., Guo, J., Lan, Y., Xu, J., & Cheng, X. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10338 · Cited by 28
The Word Analogy Testing Caveat
Schluter, N. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2039 · Cited by 28
A comparative analysis of text classification for Turkish language
Yıldırım, S., & Yıldız, T. (2018) · Pamukkale University Journal of Engineering Sciences · doi:10.5505/pajes.2018.15931 · Cited by 28
Learning Multi-Modal Word Representation Grounded in Visual Context
Zablocki, É., Piwowarski, B., Soulier, L., & Gallinari, P. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11939 · Cited by 28
"Brute-Force Sentence Pattern Extortion from Harmful Messages for Cyberbullying Detection"
Ptaszynski, M. E., Lempa, P., Masui, F., Kimura, Y., Rzepka, R., Araki, K., Wroczyński, M., Samurai Labs, P., Leliwa, G., & Samurai Labs, P. (2019) · Journal of the Association for Information Systems · doi:10.17705/1jais.00562 · Cited by 28
Syed, K., Sleeman, W. C., Ivey, K., Hagan, M. P., Palta, J. R., Kapoor, R., & Ghosh, P. (2020) · Healthcare · doi:10.3390/healthcare8020120 · Cited by 27
Liu, F., Zheng, L., & Zheng, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.084 · Cited by 32
Ferrario, A., Demiray, B., Yordanova, K., Luo, M., & Martin, M. (2020) · Journal of Medical Internet Research · doi:10.2196/19133 · Cited by 28
Exploiting complex medical data with interpretable deep learning for adverse drug event prediction
Rebane, J. T., Samsten, I., & Papapetrou, P. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101942 · Cited by 29
Concept Class Analysis: A Method for Identifying Cultural Schemas in Texts
Taylor, M. A., & Stoltz, D. S. (2020) · Sociological Science · doi:10.15195/v7.a23 · Cited by 28
The mechanism of additive composition
Tian, R., 岡崎直観, Okazaki, N., & Inui, K. (2018) · Tokyo Tech Research Repository (Tokyo Institute of Technology) · Cited by 28
Qian, K., Schmitt, M., Zheng, H., Koike, T., Han, J., Liu, J., Ji, W., Duan, J., Song, M., Yang, Z., et al. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3067605 · Cited by 28
Semantic enhanced Markov model for sequential E-commerce product recommendation
Nasir, M., & Ezeife, C. I. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-022-00343-y · Cited by 28
Sensorimotor distance: A grounded measure of semantic similarity for 800 million concept pairs
Wingfield, C., & Connell, L. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01965-7 · Cited by 28
Evaluating Neighbor Rank and Distance Measures as Predictors of Semantic Priming
Lapesa, G., & Evert, S. (2013) · Cited by 28
Exploring patterns in dictionary definitions for synonym extraction
Wang, T., & Hirst, G. (2011) · Natural Language Engineering · doi:10.1017/s1351324911000210 · Cited by 28
Synonym Analysis for Predicate Expansion
Abedjan, Z., & Naumann, F. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38288-8_10 · Cited by 29
Harvesting Parallel News Streams to Generate Paraphrases of Event Relations
Zhang, C., & Weld, D. S. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1183 · Cited by 28
Panta rei: Tracking Semantic Change with Distributional Semantics in Ancient Greek
Rodda, M. A., Senaldi, M. S. G., & Lenci, A. (2017) · Italian Journal of Computational Linguistics · doi:10.4000/ijcol.421 · Cited by 28
Gender Detection of Twitter Users Based on Multiple Information Sources
Vicente, M., Batista, F., & Carvalho, J. P. (2018) · Studies in computational intelligence · doi:10.1007/978-3-030-01632-6_3 · Cited by 29
Capturing and measuring thematic relatedness
Kacmajor, M., & Kelleher, J. D. (2019) · Language Resources and Evaluation · doi:10.1007/s10579-019-09452-w · Cited by 28
Ahn, I., Na, W., Kwon, O., Yang, D. H., Park, G., Gwon, H., Kang, H. J., Jeong, Y. U., Yoo, J., Kim, Y., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01392-2 · Cited by 26
Contextualized Keyword Representations for Multi-modal Retinal Image Captioning
Huang, J.-H., Wu, T.-W., & Worring, M. (2021) · International Conference on Multimedia Retrieval · doi:10.1145/3460426.3463667 · Cited by 28
On Clustering and Evaluation of Narrow Domain Short-Test Corpora
AVENDAÑO, D. E. P. (2008) · doi:10.4995/thesis/10251/2641 · Cited by 28
Fitting, Not Clashing! A Distributional Semantic Model of Logical Metonymy
Zarcone, A., Lenci, A., Padó, S., & Utt, J. (2013) · Cited by 27
Frame Identification as Categorization: Exemplars vs Prototypes in Embeddingland
Sikos, J., & Padó, S. (2019) · International Conference on Computational Semantics (IWCS) · doi:10.18653/v1/w19-0425 · Cited by 26
Overviewing Important Aspects of the Last Twenty Years of Research in Comparable Corpora
Sharoff, S., Rapp, R., & Zweigenbaum, P. (2013) · Springer eBooks · doi:10.1007/978-3-642-20128-8_1 · Cited by 27
Parallelograms revisited: Exploring the limitations of vector space models for simple analogies
Peterson, J. C., Chen, D., & Griffiths, T. L. (2020) · Cognition · doi:10.1016/j.cognition.2020.104440 · Cited by 27
A Computational Exploration of Exaggeration
Troiano, E., Strapparava, C., Özbal, G., & Tekiroğlu, S. S. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1367 · Cited by 27
Fitzgerald, J., Elmore, J., Relyea, J. E., & Stenner, A. J. (2019) · Journal of Educational Psychology · doi:10.1037/edu0000386 · Cited by 27
Zhu, R., Tu, X., & Huang, J. X. (2019) · Studies in big data · doi:10.1007/978-3-030-33966-1_13 · Cited by 29
The Croatian psycholinguistic database: Estimates for 6000 nouns, verbs, adjectives and adverbs
Peti‐Stantić, A., Anđel, M., Gnjidić, V., Keresteš, G., Ljubešić, N., Masnikosa, I., Tonković, M., Tušek, J., Willer‐Gold, J., & Stanojević, M. (2021) · Behavior Research Methods · doi:10.3758/s13428-020-01533-x · Cited by 33
Activity Image-to-Video Retrieval by Disentangling Appearance and Motion
Liu, L., Li, J., Niu, L., Xu, R., & Zhang, L. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16312 · Cited by 28
Word2vec Skip-Gram Dimensionality Selection via Sequential Normalized Maximum Likelihood
Hung, P. T., & Yamanishi, K. (2021) · Entropy · doi:10.3390/e23080997 · Cited by 27
Kiefer, S. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.014 · Cited by 27
Elce, V., Handjaras, G., & Bernardi, G. (2021) · Clocks & Sleep · doi:10.3390/clockssleep3030035 · Cited by 30
The news media and its audience: Agenda setting on organic food in the United States and Germany
Danner, H., Hagerer, G., Pan, Y., & Groh, G. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131503 · Cited by 34
Metadata
- 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
- 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
