This is Oxford University Press's most comprehensive single-volume dictionary, with 170,000 entries covering all varieties of English worldwide. The NODE data set constitutes a fully integrated range of formal data types suitable for language engineering and NLP applications: It is available in XML or SGML. - Source dictionary data. The NODE data set includes all the information present in the New Oxford Dictionary of English itself, such as definition text, example sentences, grammatical indicators, and encyclopaedic material. - Morphological data. Each NODE lemma (both headwords and subentries) has a full listing of all possible syntactic forms (e.g. plurals for nouns, inflections for verbs, comparatives and superlatives for adjectives), tagged to show their syntactic relationships. Each form has an IPA pronunciation. Full morphological data is also given for spelling variants (e.g. typical American variants), and a system of links enables straightforward correlation of variant forms to standard forms. The data set thus provides robust support for all look-up routines, and is equally viable for applications dealing with American and British English. - Phrases and idioms. The NODE data set provides a rich and flexible codification of over 10,000 phrasal verbs and other multi-word phrases. It features comprehensive lexical resources enabling applications to identify a phrase not only in the form listed in the dictionary but also in a range of real-world variations, including alternative wording, variable syntactic patterns, inflected verbs, optional determiners, etc. - Subject classification. Using a categorization scheme of 200 key domains, over 80,000 words and senses have been associated with particular subject areas, from aeronautics to zoology. As well as facilitating the extraction of subject-specific sub-lexicons, this also provides an extensive resource for document categorization and information retrieval. - Semantic relationships. The relationships between every noun and noun sense in the dictionary are being codified using an extensive semantic taxonomy on the model of the Princeton WordNet project. (Mapping to WordNet 1.7 is supported.) This structure allows elements of the basic lexical database to function as a formal knowledge database, enabling functionality such as sense disambiguation and logical inference. - Derived from the detailed and authoritative corpus-based research of Oxford University Press's lexicographic team, the NODE data set is a powerful asset for any task dealing with real-world contemporary English usage. By integrating a number of different data types into a single structure, it creates a coherent resource which can be queried along numerous axes, allowing open-ended exploitation by many kinds of language-related applications.

LScD (Leicester Scientific Dictionary)April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk/suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny Mirkes[Version 3] The third version of LScD (Leicester Scientific Dictionary) is created from the updated LSC (Leicester Scientific Corpus) - Version 2*. All pre-processing steps applied to build the new version of the dictionary are the same as in Version 2** and can be found in description of Version 2 below. We did not repeat the explanation. After pre-processing steps, the total number of unique words in the new version of the dictionary is 972,060. The files provided with this description are also same as described as for LScD Version 2 below.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2** Suzen, Neslihan (2019): LScD (Leicester Scientific Dictionary). figshare. Dataset. https://doi.org/10.25392/leicester.data.9746900.v2[Version 2] Getting StartedThis document provides the pre-processing steps for creating an ordered list of words from the LSC (Leicester Scientific Corpus) [1] and the description of LScD (Leicester Scientific Dictionary). This dictionary is created to be used in future work on the quantification of the meaning of research texts. R code for producing the dictionary from LSC and instructions for usage of the code are available in [2]. The code can be also used for list of texts from other sources, amendments to the code may be required.LSC is a collection of abstracts of articles and proceeding papers published in 2014 and indexed by the Web of Science (WoS) database [3]. Each document contains title, list of authors, list of categories, list of research areas, and times cited. The corpus contains only documents in English. The corpus was collected in July 2018 and contains the number of citations from publication date to July 2018. The total number of documents in LSC is 1,673,824.LScD is an ordered list of words from texts of abstracts in LSC.The dictionary stores 974,238 unique words, is sorted by the number of documents containing the word in descending order. All words in the LScD are in stemmed form of words. The LScD contains the following information:1.Unique words in abstracts2.Number of documents containing each word3.Number of appearance of a word in the entire corpusProcessing the LSCStep 1.Downloading the LSC Online: Use of the LSC is subject to acceptance of request of the link by email. To access the LSC for research purposes, please email to ns433@le.ac.uk. The data are extracted from Web of Science [3]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.Step 2.Importing the Corpus to R: The full R code for processing the corpus can be found in the GitHub [2].All following steps can be applied for arbitrary list of texts from any source with changes of parameter. The structure of the corpus such as file format and names (also the position) of fields should be taken into account to apply our code. The organisation of CSV files of LSC is described in README file for LSC [1].Step 3.Extracting Abstracts and Saving Metadata: Metadata that include all fields in a document excluding abstracts and the field of abstracts are separated. Metadata are then saved as MetaData.R. Fields of metadata are: List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.Step 4.Text Pre-processing Steps on the Collection of Abstracts: In this section, we presented our approaches to pre-process abstracts of the LSC.1.Removing punctuations and special characters: This is the process of substitution of all non-alphanumeric characters by space. We did not substitute the character “-” in this step, because we need to keep words like “z-score”, “non-payment” and “pre-processing” in order not to lose the actual meaning of such words. A processing of uniting prefixes with words are performed in later steps of pre-processing.2.Lowercasing the text data: Lowercasing is performed to avoid considering same words like “Corpus”, “corpus” and “CORPUS” differently. Entire collection of texts are converted to lowercase.3.Uniting prefixes of words: Words containing prefixes joined with character “-” are united as a word. The list of prefixes united for this research are listed in the file “list_of_prefixes.csv”. The most of prefixes are extracted from [4]. We also added commonly used prefixes: ‘e’, ‘extra’, ‘per’, ‘self’ and ‘ultra’.4.Substitution of words: Some of words joined with “-” in the abstracts of the LSC require an additional process of substitution to avoid losing the meaning of the word before removing the character “-”. Some examples of such words are “z-test”, “well-known” and “chi-square”. These words have been substituted to “ztest”, “wellknown” and “chisquare”. Identification of such words is done by sampling of abstracts form LSC. The full list of such words and decision taken for substitution are presented in the file “list_of_substitution.csv”.5.Removing the character “-”: All remaining character “-” are replaced by space.6.Removing numbers: All digits which are not included in a word are replaced by space. All words that contain digits and letters are kept because alphanumeric characters such as chemical formula might be important for our analysis. Some examples are “co2”, “h2o” and “21st”.7.Stemming: Stemming is the process of converting inflected words into their word stem. This step results in uniting several forms of words with similar meaning into one form and also saving memory space and time [5]. All words in the LScD are stemmed to their word stem.8.Stop words removal: Stop words are words that are extreme common but provide little value in a language. Some common stop words in English are ‘I’, ‘the’, ‘a’ etc. We used ‘tm’ package in R to remove stop words [6]. There are 174 English stop words listed in the package.Step 5.Writing the LScD into CSV Format: There are 1,673,824 plain processed texts for further analysis. All unique words in the corpus are extracted and written in the file “LScD.csv”.The Organisation of the LScDThe total number of words in the file “LScD.csv” is 974,238. Each field is described below:Word: It contains unique words from the corpus. All words are in lowercase and their stem forms. The field is sorted by the number of documents that contain words in descending order.Number of Documents Containing the Word: In this content, binary calculation is used: if a word exists in an abstract then there is a count of 1. If the word exits more than once in a document, the count is still 1. Total number of document containing the word is counted as the sum of 1s in the entire corpus.Number of Appearance in Corpus: It contains how many times a word occurs in the corpus when the corpus is considered as one large document.Instructions for R CodeLScD_Creation.R is an R script for processing the LSC to create an ordered list of words from the corpus [2]. Outputs of the code are saved as RData file and in CSV format. Outputs of the code are:Metadata File: It includes all fields in a document excluding abstracts. Fields are List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.File of Abstracts: It contains all abstracts after pre-processing steps defined in the step 4.DTM: It is the Document Term Matrix constructed from the LSC[6]. Each entry of the matrix is the number of times the word occurs in the corresponding document.LScD: An ordered list of words from LSC as defined in the previous section.The code can be used by:1.Download the folder ‘LSC’, ‘list_of_prefixes.csv’ and ‘list_of_substitution.csv’2.Open LScD_Creation.R script3.Change parameters in the script: replace with the full path of the directory with source files and the full path of the directory to write output files4.Run the full code.References[1]N. Suzen. (2019). LSC (Leicester Scientific Corpus) [Dataset]. Available: https://doi.org/10.25392/leicester.data.9449639.v1[2]N. Suzen. (2019). LScD-LEICESTER SCIENTIFIC DICTIONARY CREATION. Available: https://github.com/neslihansuzen/LScD-LEICESTER-SCIENTIFIC-DICTIONARY-CREATION[3]Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4]A. Thomas, "Common Prefixes, Suffixes and Roots," Center for Development and Learning, 2013.[5]C. Ramasubramanian and R. Ramya, "Effective pre-processing activities in text mining using improved porter’s stemming algorithm," International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 12, pp. 4536-4538, 2013.[6]I. Feinerer, "Introduction to the tm Package Text Mining in R," Accessible en ligne: https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf, 2013.

One of our flagship datasets, the American English data, is expertly curated and linguistically annotated by professionals, with annual updates to ensure accuracy and relevance. The below datasets in American English are available for license:

1. American English Monolingual Dictionary Data
2. American English Synonyms and Antonyms Data
3. American English Pronunciations with Audio

Key Features (approximate numbers):

1. American English Monolingual Dictionary Data

Our American English Monolingual Dictionary Data is the foremost authority on American English, including detailed tagging and labelling covering parts of speech (POS), grammar, region, register, and subject, providing rich linguistic information. Additionally, all grammar and usage information is present to ensure relevance and accuracy.

• Headwords: 140,000
• Senses: 222,000
• Sentence examples: 140,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. American English Synonyms and Antonyms Data

The American English Synonyms and Antonyms Dataset is a leading resource offering comprehensive and up-to-date coverage of word relationships in contemporary American English. It includes rich linguistic detail such as precise definitions and part-of-speech (POS) tags, making it an essential asset for developing AI systems and language technologies that require deep semantic understanding.

• Synonyms: 600,000
• Antonyms: 22,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. American English Pronunciations with Audio (word-level)

This dataset provides IPA transcriptions and mapped audio files for words in contemporary American English, with a focus on US speaker usage. It includes syllabified transcriptions, variant spellings, part-of-speech tags, and pronunciation group identifiers. Audio files are supplied separately and linked where available – ideal for TTS, ASR, and pronunciation modeling.

• Transcriptions (IPA): 250,000
• Audio files: 180,000
• Format: XLSX (for transcriptions), MP3 and WAV (audio files)
• Updated frequency: annually

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.

The dictionary includes about 1650 terms and concepts (in Armenian, Russian and English) used in forest and landscaping sectors with a brief explanation in Armenian.Citation: J.H. Vardanyan, H.T. Sayadyan, Armenian-Russian-English Dictionary of Forest Terminology, Publishing House of the Institute of Botany of NAS RA, Yerevan, 2008.

Our British English language datasets are meticulously curated and annotated by experienced linguistics and language experts, ensuring exceptional accuracy, consistency, and linguistic depth. The below datasets in British English are available for license:

1. British English Monolingual Dictionary Data
2. British English Synonyms and Antonyms Data
3. British English Pronunciations with Audio

Key Features (approximate numbers):

1. British English Monolingual Dictionary Data

Our British English monolingual dataset delivers clear, reliable definitions and authentic usage examples, featuring a high volume of headwords and in-depth coverage of the British English variant of English. As one of the world’s most authoritative lexical resources, it’s trusted by leading academic, AI, and language technology organizations.

• Headwords: 146,000
• Senses: 230,000
• Sentence examples: 149,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: twice a year

1. British English Synonyms and Antonyms Data

This British English language dataset offers a rich collection of synonyms and antonyms, accompanied by detailed definitions and part-of-speech (POS) annotations, making it a comprehensive resource for NLP tasks such as semantic search, word sense disambiguation, and language generation.

• Synonyms: 600,000
• Antonyms: 22,000
• Usage Examples: 39,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing)
• Updated frequency: annually

1. British English Pronunciations with audio (word-level)

This dataset provides IPA transcriptions and mapped audio files for words in contemporary British English, with a focus on UK speaker usage. It includes syllabified transcriptions, variant spellings, part-of-speech tags, and pronunciation group identifiers. Audio files are supplied separately and linked where available – ideal for TTS, ASR, and pronunciation modeling.

• Transcriptions (IPA): 250,000
• Audio files: 180,000
• Format: XLSX (for transcriptions), MP3 and WAV (audio files)
• Updated frequency: annually

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.

Dictionary Network Graph

Contains a graph representation of the the English dictionary, where each word is a node and its edges are defined when a word appears in a definition. The JSON file is of the form: JSON {word: [Each, word, in, its, definition] ... }

Use this dataset to explore the structure of natural language!

This dataset contains resources that link to Online Marshallese-English Dictionaries.

You can read more about this dataset and how to use it in the English Contractor Monthly General Dental and Orthodontic Activity guidance documentation (ODT: 234KB). You can view all definitions for the fields included in the dataset in the English Contractor Monthly Orthodontic Activity Data Dictionary (XLSX: 10KB) We also publish the English Contractor Monthly General Dental Activity dataset.

Discover our expertly curated language datasets in the LATAM Data Suite. Compiled and annotated by language and linguistic experts, this suite offers high-quality resources tailored to your needs. This suite includes:

• Monolingual and Bilingual Dictionary Data Featuring headwords, definitions, word senses, part-of-speech (POS) tags, and semantic metadata.

• Sentences Curated examples of real-world usage with contextual annotations.

• Synonyms & Antonyms Lexical relations to support semantic search, paraphrasing, and language understanding.

• Audio Data Native speaker recordings for TTS and pronunciation modeling.

• Word Lists Frequency-ranked and thematically grouped lists.

Learn more about the datasets included in the data suite:

1. Portuguese Monolingual Dictionary Data
2. Portuguese Bilingual Dictionary Data
3. Spanish Monolingual Dictionary Data
4. Spanish Bilingual Dictionary Data
5. Spanish Sentences Data
6. Spanish Synonyms and Antonyms Data
7. Spanish Audio Data
8. Spanish Word List Data
9. American English Monolingual Dictionary Data
10. American English Synonyms and Antonyms Data
11. American English Pronunciations with Audio

Key Features (approximate numbers):

1. Portuguese Monolingual Dictionary Data

Our Portuguese monolingual covers both European and Latin American varieties, featuring clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Portuguese language.

• Headwords: 143,600
• Senses: 285,500
• Sentence examples: 69,300
• Format: XML format
• Delivery: Email (link-based file sharing)

1. Portuguese Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Portuguese and from Portuguese to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality that span both European and Latin American Portuguese varieties.

• Translations: 300,000
• Senses: 158,000
• Example sentences: 117,800
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Monolingual Dictionary Data

Our Spanish monolingual reliably offers clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Spanish language.

• Headwords: 73,000
• Senses: 123,000
• Sentence examples: 104,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Spanish and from Spanish to English. It is annually reviewed and updated by our in-house team of language experts. Offers significant coverage of the language, providing a large volume of translated words of excellent quality.

• Translations: 221,300
• Senses: 103,500
• Example sentences: 74,500
• Example translations: 83,800
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Sentences Data

Spanish sentences retrieved from corpus are ideal for NLP model training, presenting approximately 20 million words. The sentences provide a great coverage of Spanish-speaking countries and are accordingly tagged to a particular country or dialect.

• Sentences volume: 1,840,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API

1. Spanish Synonyms and Antonyms Data

This Spanish language dataset offers a rich collection of synonyms and antonyms, accompanied by detailed definitions and part-of-speech (POS) annotations, making it a comprehensive resource for building linguistically aware AI systems and language technologies.

• Synonyms: 127,700
• Antonyms: 9,500
• Format: XML format
• Delivery: Email (link-based file sharing)
• Updated frequency: annually

1. Spanish Audio Data (word-level)

Curated word-level audio data for the Spanish language, which covers all varieties of world Spanish, providing rich dialectal diversity in the Spanish language.

• Audio files: 20,900
• Format: XLSX (for index), MP3 and WAV (audio files)

1. Spanish Word List Data

This language data contains a carefully curated and comprehensive list of 450,000 Spanish words.

• Wordforms: 450,000
• Format: CSV and TXT formats
• Delivery: Email (link-based file sharing)

1. American English Monolingual Dictionary Data

Our American English Monolingual Dictionary Data is the foremost authority on American English, including detailed tagging and labelling covering parts of speech (POS), grammar, region, register, and subject, providing rich linguistic information. Additionally, all grammar and usage information is present to ensure rel...

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for all MSOAs and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsProficiency in EnglishThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by their proficiency in English. The estimates are as at Census Day, 21 March 2021.Definition: How well people whose main language is not English (English or Welsh in Wales) speak English.This dataset provides details for the MSOAs of Leicester city.

Explore the Arabic Pronunciation Dictionary Dataset for accurate pronunciation data, a valuable resource for language learners.

This dataset is a curated list of frequently used English Dictionary Words. This dataset is mainly designed to help a user solve the Wordle Game in a much faster way.

The English Contractor Monthly General Dental Activity dataset provides information about the general dental activity carried out by dentists on behalf of the NHS in England. Published monthly, the data is released at individual dental contract level and only relate to contracts that provide General NHS Dental Services. However, they do contain information on all service lines held by that contract in relation to the UDA and FP17s delivered. This dataset allows you to look at the activity provided for each financial year, monthly at national, commissioner and contract level. You can compare the general activity delivered to that commissioned in the contract data. This will give you an idea of how areas of contracts are performing. The dataset will not give the whole picture of NHS dentistry in England as it is possible for other dental services to commissioned locally. You can read more about this dataset and how to use it in the English Contractor Monthly General Dental and Orthodontic Activity guidance documentation (ODT: 234KB). You can view all definitions for the fields included in the dataset in the English Contractor Monthly General Dental Activity Data Dictionary (XLSX: 12KB) We also publish the English Contractor Monthly Orthodontic Dental Activity dataset. Overview of Service

Dataset on Dravidian languages, focusing on Tulu, accompanying the master's thesis of Lindgren (2023). Includes data from the author's fieldwork, contributions from other authors, and data adapted from the Kolipakam et al. (2018) study.

The author collected a set of lexical items for comparative analysis to address the classification of Tulu, Koraga, and Bellari within the Dravidian language family. This dataset included 114 comparative concepts previously collected for the study of Bellari (Bhat, 1971), the Leipzig-Jakarta list of lexical items, a subset of the 100-word Swadesh list not present in the Leipzig-Jakarta list, and counting words from 1 to 10, and a more extensive list of pronouns, totalling 231 comparative concepts. Word lists were gathered from various sources, including grammars and dictionaries, for Bellari (Bhat, 1971), four Koraga varieties (Onti, Tappu, and Mudu from Bhat, 1971; Ande from Shetty, 2008), Kannada (Kittel, 1894; Učida, Rajapurohit & Takashima, 2018; Spencer, 1950; Zydenbos, 2011; Sridhar, 1990), Malayalam (Moag & Moag, 1967; Asher & Kumari, 1967; Sudha, 1984; Jiang, 2010), Tamil (Borin et al., 2013), Byari (Upadhyaya, 2011), and Pattapu (IRA, 2013), and three Tulu varieties. The Tulu word lists included those from M. M. Bhat (1967), a dictionary of Tulu including words from multiple dialects, a Madhwa Brahmin wordlist collected from Bhatt (1971), and a wordlist from data collected through the author's fieldwork. Data for several other languages, namely Telugu, Koya, Kolami, Gondi, Parji, Ollari Gadba, Kuwi, Kurukh, Malto, Brahui, Yeruva, Kodava, Badga, Toda, Kota, and Betta Kurumba, were included from Kolipakam et al. (2018), as well as additional data for Kannada, Malayalam, Tamil, and Tulu from the same source. The latter’s Kannada, Malayalam, Tamil, and Tulu wordlists were added as separate doculects.

Concepts not given in Concepticon are marked by an initial asterisk (e.g., "*BETEL LEAF"). Most of these are concepts distinguishing pronouns in the languages, such as marking distinctions between remote and proximate (e.g., "*3SG.I.R" and "*3SG.I.P"), which the author considered essential for comparing the languages also due to the conservativeness of some forms and phonemes (e.g., the presence of /a/ for remote and /i/ for proximate in most languages). The labels for pronoun concepts not given in Concepticon are built with the following constituents:

"2SG" : second singular

"3PL" : third plural

"3SG" : third singular

"A" : animate

"F" : female

"H" : honorific

"I" : inanimate

"M" : male

"P" : proximate

"R" : remote

References

Asher, R. E. & Kumari, T. C. (1997). Malayalam. Descriptive Grammars Series, Descriptive Grammars. London & New York: Routledge.

Bhat, D. N. S. (1971). The Koraga language. Poona: Deccan College.

Bhat, M. M. (1967). Tulu-English dictionary. Madras: University of Madras.

Bhatt, S. L. (1971). A Grammar of Tulu (A Dravidian Language). Ann Arbor: UMI. (Doctoral dissertation, Madison: University of Wisconsin.)

Borin, L.; Comrie, B. & Saxena, A. (2013). The Intercontinental Dictionary Series – a rich and principled database for language comparison. In Borin, L. & Saxena, A. (eds) Approaches to Measuring Linguistic Differences, 285–302. Berlin: De Gruyter Mouton.

IRA ISO 639-3 Registration Authority. Change Request Number 2013-020: adopted create ptq. Dallas: SIL International.

Jiang, H. (2010). Malayalam: a Grammatical Sketch and a Text. Houston: Department of Linguistics, Rice University.

Kittel, F. (1894). A Kannaḍa-English dictionary. Mangalore: Basel Mission Book and Tract Depository.

Kolipakam, V.; Jordan, F. M.; Dunn, M.; Greenhill, S. J.; Bouckaert, R.; Gray, R. D. & Verkerk, A. (2018). A Bayesian phylogenetic study of the Dravidian language family. Royal Society open science, 5(3), 171504. http://doi.org/10.1098/rsos.171504

Moag, R. & Moag, R. (1967). A course in Colloquial Malayalam. Milwaukee, Wisconsin: Peace Corps.

Shetty, R. (2008). Koraga Grammar. Kuppam: Department of Dravidian Computational Linguistics, Dravidian University.

Spencer, H. (1950). A Kanarese Grammar. Mysore City: Wesley Press.

Sridhar, S. N. 1990. Kannada. (Descriptive Grammars Series, Descriptive Grammars.) London & New York: Routledge.

Sudha, B. B. (1984). Case grammar of standard Malayalam. (Doctoral dissertation, Trivandrum: University of Kerala.)

Učida, N.; Rajapurohit, B. B. & Takashima, J. (2018). Kannada-English Etymological Dictionary. Tokyo: ILCAA.

Upadhyaya, S. P. (2011). Beary language: descriptive grammar and comparative study. Mangalore: Karnataka Beary Sahithya Academy.

Zydenbos, R. (2011). A grammar of Kannada. Ms.

A multidimensional dataset created for the department of Cauca based on public data sources is published. The dataset integrates the 4 FAO food security dimensions: physical availability of food, economic and physical access to food, food utilization, and the sustainability of the dimensions mentioned above. It also allows analysis of different variables such as nutritional, socioeconomic, climatic, sociodemographic, among others with statistical techniques or temporal analysis. The dataset can also be used for analysis and extraction of characteristics with computer vision techniques from satellite images, or multimodal machine learning with data of a different nature (images and tabular data).

The dataset Contains the folders: - Multidimensional dataset of Cauca/: Here are the tabular data of the municipalities of the department of Cauca. The folder contains the files: 1. dictionary(English).xlsx: The dictionary of the static variables for each municipality of Cauca in english. 2. dictionary(Español): The dictionary of the static variables for each municipality of Cauca in spanish. 3. dictionary(English).xlsx: The dictionary of the static variables for each municipality of Cauca in english. 4. MultidimensionalDataset_AllMunicipalities.csv: Nutritional, climatic, sociodemographic, socioeconomic and agricultural data of the 42 municipalities of the department of Cauca, although with some null values due to the lack of data in nutrition surveys of some municipalities. - Satellite Images Popayán/: Here are the monthly Landsat 8 satellite images of the municipality of Popayán in Cauca. The folder contains the folders: 1. RGB/: Contains the RGB images of the municipality of Popayán in the department of Cauca. It contains RGB images of Popayán from April 2013 to December 2020 in a resolution of 15 m / px. The title of each image is image year_month.png. 1. 6 Band Images/: Contains images of Landsat 8 using bands 1 to 8 to generate images of the municipality of Popayán in the department of Cauca. It contains 6 band images in a tif format of Popayán from April 2013 to December 2020 in a resolution of 15 m / px. The title of each image is image year_month.tif.

HindEnCorp parallel texts (sentence-aligned) come from the following sources:

Tides, which contains 50K sentence pairs taken mainly from news articles. This dataset was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy, 2008).

Commentaries by Daniel Pipes contain 322 articles in English written by a journalist Daniel Pipes and translated into Hindi.

EMILLE. This corpus (Baker et al., 2002) consists of three components: monolingual, parallel and annotated corpora. There are fourteen monolingual sub- corpora, including both written and (for some lan- guages) spoken data for fourteen South Asian lan- guages. The EMILLE monolingual corpora contain in total 92,799,000 words (including 2,627,000 words of transcribed spoken data for Bengali, Gujarati, Hindi, Punjabi and Urdu). The parallel corpus consists of 200,000 words of text in English and its accompanying translations into Hindi and other languages.

Smaller datasets as collected by Bojar et al. (2010) include the corpus used at ACL 2005 (a subcorpus of EMILLE), a corpus of named entities from Wikipedia (crawled in 2009), and Agriculture domain parallel corpus.



For the current release, we are extending the parallel corpus using these sources:

Intercorp (Čermák and Rosen,2012) is a large multilingual parallel corpus of 32 languages including Hindi. The central language used for alignment is Czech. Intercorp’s core texts amount to 202 million words. These core texts are most suitable for us because their sentence alignment is manually checked and therefore very reliable. They cover predominately short sto- ries and novels. There are seven Hindi texts in Inter- corp. Unfortunately, only for three of them the English translation is available; the other four are aligned only with Czech texts. The Hindi subcorpus of Intercorp contains 118,000 words in Hindi.

TED talks 3 held in various languages, primarily English, are equipped with transcripts and these are translated into 102 languages. There are 179 talks for which Hindi translation is available.

The Indic multi-parallel corpus (Birch et al., 2011; Post et al., 2012) is a corpus of texts from Wikipedia translated from the respective Indian language into English by non-expert translators hired over Mechanical Turk. The quality is thus somewhat mixed in many respects starting from typesetting and punctuation over capi- talization, spelling, word choice to sentence structure. A little bit of control could be in principle obtained from the fact that every input sentence was translated 4 times. We used the 2012 release of the corpus.

Launchpad.net is a software collaboration platform that hosts many open-source projects and facilitates also collaborative localization of the tools. We downloaded all revisions of all the hosted projects and extracted the localization (.po) files.

Other smaller datasets. This time, we added Wikipedia entities as crawled in 2013 (including any morphological variants of the named entitity that appears on the Hindi variant of the Wikipedia page) and words, word examples and quotes from the Shabdkosh online dictionary.

Our Spanish language datasets are carefully compiled and annotated by language and linguistic experts; you can find them available for licensing:

1. Spanish Monolingual Dictionary Data
2. Spanish Bilingual Dictionary Data
3. Spanish Sentences Data
4. Synonyms and Antonyms Data
5. Audio Data
6. Spanish Word List Data

Key Features (approximate numbers):

1. Spanish Monolingual Dictionary Data

Our Spanish monolingual reliably offers clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Spanish language.

• Headwords: 73,000
• Senses: 123,000
• Sentence examples: 104,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Spanish and from Spanish to English. It is annually reviewed and updated by our in-house team of language experts. Offers significant coverage of the language, providing a large volume of translated words of excellent quality.

• Translations: 221,300
• Senses: 103,500
• Example sentences: 74,500
• Example translations: 83,800
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Sentences Data

Spanish sentences retrieved from the corpus are ideal for NLP model training, presenting approximately 20 million words. The sentences provide a great coverage of Spanish-speaking countries and are accordingly tagged to a particular country or dialect.

• Sentences volume: 1,840,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API

1. Spanish Synonyms and Antonyms Data

This Spanish language dataset offers a rich collection of synonyms and antonyms, accompanied by detailed definitions and part-of-speech (POS) annotations, making it a comprehensive resource for building linguistically aware AI systems and language technologies.

• Synonyms: 127,700
• Antonyms: 9,500
• Format: XML format
• Delivery: Email (link-based file sharing)
• Updated frequency: annually

1. Spanish Audio Data (word-level)

Curated word-level audio data for the Spanish language, which covers all varieties of world Spanish, providing rich dialectal diversity in the Spanish language.

• Audio files: 20,900
• Format: XLSX (for index), MP3 and WAV (audio files)

1. Spanish Word List Data

This language data contains a carefully curated and comprehensive list of 450,000 Spanish words.

• Wordforms: 450,000
• Format: CSV and TXT formats
• Delivery: Email (link-based file sharing)

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Oxford.Languages@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Oxford.Languages@oup.com to explore pricing options and discover how our language data can support your goals.

Eurostat data contains many indicators (short-term, structural, theme-specific and others) on the EU-28 and the Eurozone, the Member States and their partners. The database of Eurostat contains always the latest version of the datasets meaning that there is no versioning on the data. Datasets are updated twice a day, at 11:00 and at 23:00, in case new data is available or because of structural change. It is possible to access the datasets through SDMX Web Services, as well as through Json and Unicode Web Services. SDMX Web Services are a programmatic access to Eurostat data, with the possibility to: get a complete list of publicly available datasets; detail the complete structure definition of a given dataset; download a subset of a given dataset or a full dataset. SDMX Web Services: provide access to datasets listed under database by themes, and predefined tables listed under tables by themes; provide data in SDMX 2.0 and 2.1 formats; support both Representation State Transfer (REST) and Simple Object Access Protocol (SOAP) protocols; return responses in English language only; are free of charge. The JSON & UNICODE Web Services are a programmatic access to Eurostat data, with the possibility to download a subset of a given dataset. This operation allows customizing requests for data. You can filter on dimensions to retrieve specific data subsets. The JSON & UNICODE Web Services: provide data in JSON-stat and UNICODE formats; support only Representation State Transfer (REST) protocol; deliver responses in English, French and German language; are free of charge.

Our German language datasets are carefully compiled and annotated by language and linguistic experts. The below datasets in German are available for license:

1. German Monolingual Dictionary Data
2. German Bilingual Dictionary Data
3. German Word List Data

Key Features (approximate numbers):

1. German Monolingual Dictionary Data

Our German monolingual features clear definitions, headwords, examples, and comprehensive coverage of the German language spoken today.

• Headwords: 85,500
• Senses: 78,000
• Sentence examples: 55,000
• Format: XML format
• Delivery: Email (link-based file sharing)

1. German Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to German and from German to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality.

• Translations: 393,000
• Senses: 207,500
• Example sentences: 129,500
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. German Word List Data

This language data contains a carefully curated and comprehensive list of 338,000 German words.

• Wordforms: 338,000
• Format: CSV and TXT formats
• Delivery: Email (link-based file sharing)

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.

Dataset contains basic descriptive data on territorial elements and units of territorial registration, in which at least one attribute in selected day have changed. Dataset contains no spatial location (polygons, definition lines and centroids of RÚIAN elements). The file contains following elements (in case they have changed): state, cohesion region, higher territorial self-governing entity (VÚSC), municipality with extended competence (ORP), authorized municipal office (POU), regions (old ones – defined in 1960), county, municipality, municipality part, town district (MOMC), Prague city district (MOP), town district of Prague (SOP), cadastral units and basic urban units (ZSJ), streets, building objects and address points. Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on RÚIAN (Register of Territorial Identification, Addresses and Real Estates). Data is created every day (in case any change occurred) in RÚIAN exchange format (VFR), which is based on XML language and fulfils the GML 3.2.1 standard (according to ISO 19136:2007). Dataset is compressed (ZIP) for downloading. More in the Act No. 111/2009 Coll., on the Basic Registers, in Decree No. 359/2011 Coll., on the Basic Register of Territorial Identification, Addresses and Real Estates.