British National Corpus

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The British National Corpus (BNC) is a 100-million-word text corpus of samples of written and spoken English from a wide range of sources.[1][2][3] The corpus covers British English of the late 20th century from a wide variety of genres with the intention that it be a representative sample of spoken and written British English of that time.


The project to create the BNC involved the collaboration of three publishers (with the Oxford University Press as the lead collaborator, Longman and W. & R. Chambers), two universities (the University of Oxford and Lancaster University) and the British Library.[2]

The creation of the BNC started in 1991 under the management of the BNC consortium and the project was finished by 1994. There have been no additions of new samples after 1994 but the BNC underwent slight revisions before the release of the second edition BNC World (2001) and the third edition BNC XML Edition (2007).[4]


The BNC was the vision of computer linguists whose goal was a corpus (collection of texts) of modern (at the time of building the corpus), naturally occurring language in the form of speech and text or writing that could be analyzed by a computer. Hence, it was compiled as a general corpus to be made readable by computers to pave the way for automatic search and processing in the field of corpus linguistics. One of the ways BNC was to be differentiated from existing corpora at that time was to open up the data not just for the use of academic research, but to commercial and educational uses as well.[3]

The corpus was restricted to just British English and was not extended to cover World Englishes, partly because a significant portion of the cost of the project was being funded by the British government which was logically interested in supporting documentation of its own linguistic variety.[3]



The BNC is a monolingual corpus as it records samples of language use in British English only, although occasionally words and phrases from other languages may also be present. It is a synchronic corpus as only language use from the late 20th century is represented; the BNC is not meant to be a historical record of the development of British English over the ages.[4] From the beginning, those involved in the gathering of written data sought to make the BNC a balanced corpus and hence looked for data in various mediums.[3]

Components and content[edit]

Written corpus[edit]

90% of the BNC is samples of written language use. These samples were extracted from regional and national newspapers, published research journals or periodicals from various academic fields, both fiction and non-fiction books, both published and unpublished material such as leaflets, brochures, letters, essays written by students of differing academic levels, speeches, scripts and many other types of texts.[5]

Spoken corpus[edit]





BNC Baby is a sub-corpora of BNC that consists of four sets of samples, each containing one million words tagged as they are in BNC itself. The words in each sample set correspond to a specific genre label. One sample set contains spoken conversation and the other three sample sets contain written text; academic writing, fiction and newspapers respectively.[8] The latest third edition has been released and comes in the XML format.[9]

The BNC Sampler is a two-part sub-corpora, a part each for written and spoken data. Each part contains one million words. The BNC Sampler was originally used in a project to work out how to improve the tagging process for the BNC, eventually leading to the BNC World edition. Throughout the project, the BNC Sampler was improved with the gain in expertise and knowledge for tagging to make it what it is today.[10]


The BNC corpus has been tagged for grammatical information (part of speech). The tagging system named CLAWS went through improvements to yield the latest CLAWS4 system which is used for tagging the BNC. CLAWS1 was based on a Hidden Markov Model (HMM) and when employed in automatic tagging, managed to successfully tag 96% to 97% of each text analyzed. CLAWS1 was upgraded to CLAWS2 by the removal of the need for manual text processing before the texts could be ready for automatic tagging. The latest version CLAWS4 included improvements such as more powerful word-sense disambiguation (WSD) abilities, as well as being able to deal with varieties in orthography and markup language. Later work on the tagging system looked at increasing the success rates in tagging text automatically and reducing the work needed for manual processing while still maintaining effectiveness and efficiency by introducing software to do some of the manual work.[2][11] Subsequently, a new program called the Template Tagger was introduced for a corrective function. Tags indicating ambiguity were later added. Manual tagging still has to be done as CLAWS4 is still unable to deal with foreign words.[12][13]

Technical information[edit]

The corpus is marked up following the recommendations of the Text Encoding Initiative and includes full linguistic annotation and contextual information.[14]


The licence for the CLAWS4 tagger for part of speech information may be purchased to use the tagger.[15] Otherwise, a tagging service is offered at Lancaster University.[16]


An online program, BNCweb, has been developed for the BNC XML edition. The interface is designed to be easy to use and the program offers query features and functions for corpus analysis. Results and data from searches and analyses may be retrieved by users.[18]

Permission issue[edit]

The BNC was the first text corpus of its size to be made widely available. This could be attributed to the standard forms of agreement, between rights owners and the Consortium on the one hand, and between corpus users and the Consortium on the other. Intellectual property rights (IPR) owners were sought for their agreement to incorporate their materials in the corpus without any fees and shown the standard licence agreement which is relevant up till today. The acknowledgement with this arrangement may have been influenced by the originality of the concept and the prominence associated to this big idea.[6]

However, there was the problem of keeping the identity of contributors hidden without discrediting the value of their work. Any distinct allusion to the identity of contributors was largely taken down and the alternative of substituting it with a different name had been discussed. Yet this solution of using substitution was seen as being not feasible.[6]

Adding on to the earlier problem was the fact that the contributors had earlier been asked only to incorporate transcribed versions of their speech and not the speech itself. While permission could be sought from initial contributors again, the lack of success in the anonymization process meant that it would be challenging to sought materials from initial contributors again. At the same time, two factors compounded the unwillingness of IPR owners to donate their materials. Firstly, full texts were to be excluded and secondly, there was no motivation for them to disseminate information using the corpus, particularly since the corpus operates on a non-commercial basis.[6]

Problems and limitations[edit]

Overly broad categories[edit]

By 2001, the BNC still had no text categorisation for written texts beyond that of domain and no categorisation for spoken texts except by context and demographic or socio-economic classes. For example, a wide variety of imaginative texts (novels, short stories, poems, and drama scripts) were included in the BNC but such inclusions were deemed useless as researchers were unable to easily retrieve the sub-genres on which they wanted to work (e.g., poetry) because this information was omitted in the file headers or in any documentation associated with the BNC. Hence there was no way to know whether an "imaginative" text actually came from a novel, a short story, a drama script or a collection of poems (unless the title actually reflexively includes the words "a novel" or "poem").[19]

Later with the introduction of a new version in 2002, the BNC World Edition, BNC attempted to deal with this problem of overly broad categories. Besides domain, there are now 70 categories for genre for both spoken and written data. Hence, researchers are now able to specifically retrieve texts like poetry and prose.[20]

Even after these additions however, implementation is still tricky as assigning a genre or sub-genre to a text is not straightforward. The divisions are less clear for spoken data than they are for written data as there was more variation in topic and execution. Also, there will always be possible subsets of each sub-genre. The extent of the dividing of genres are pre-determined for the sake of a default but researchers are allowed the option of making the divisions more general or specific according to their needs. Categorisation is also a problem as certain texts, while deemed to belong to an interdisciplinary genre such as linguistics, include content that is subsequently categorised into either arts or science categories due to the nature of their content. Users are cautioned to be aware of such possibilities.[20]

Classification errors and misleading titles[edit]

Some texts were classified under the wrong category, usually because of a misleading title. Users cannot always rely on the titles of the files as indications of their real content: For example, many texts with "lecture" in their title are actually classroom discussions or tutorial seminars involving a very small group of people, or were popular lectures (addressed to a general audience rather than to students at an institution of higher learning).[19] One reason is that genre and sub-genre labels can only be assigned for the majority of the texts in a category. There are sub-genres within genres and for each text the content may not be uniform throughout and may span across sub-genres.[20] Also, production pressures coupled with yet insufficient information at that time led to hasty decisions, resulting in inaccuracy and inconsistency in records.[6]

Spoken discourse under-represented[edit]

The proportion of written to spoken material in the BNC is 10:1.[6] This is because the cost of collecting and transcribing in electronic form one million words of naturally occurring speech is at least 10 times higher than the cost of adding another million words of newspaper text. Most people would argue that since speech and writing are both equally important in a language, then both should be equally represented in the corpus.[6]

The BNC is not ideal for the study of many features of spoken discourse since most of its transcripts are orthographic. Paralinguistic features are only roughly indicated.[21]

Useful only in studying grammatical patterns which have distinctive lexical correlates[edit]

Despite being an excellent source of lexical information, the BNC can only really be used to study a limited set of grammatical patterns, particularly those which have distinctive lexical correlates. While it is easy enough to find all the occurrences of "enjoy", and to sort them according to the part-of-speech category of the following word, it is impossible to find all cases of verbs followed by a gerund, since the SARA index does not include part-of-speech categories such as "all verbs" or "all V-ing forms".[21]

Some lexical correlates are also too ambiguous to allow them to be used in queries: any search for restrictive relative clauses would provide the user with irrelevant data, given the number of other uses of wh- pronouns and of that in the language (not to mention the impossibility of identifying relative clauses with pronouns deletion, as in the man I saw). Particular semantic and pragmatic categories (doubt, cognisance, disagreements, summaries, etc.) are difficult to locate for the same reason.[21]

Limited situational description[edit]


Inappropriate for study of highly specific text-types or genres[edit]


Uses of the BNC[edit]

English language education[edit]



For example, the BNC was used by a group of Japanese researchers as a tool in their creation of an English language-learning website for learners of English for specific purposes (ESP).[22] The website enabled English language learners to download frequently heard and used sentence patterns, and then base their own usage of the English language on these sentence patterns. The BNC served as the source from which the frequently used expressions were extracted. In using this website, users thus relied on reference samples from the BNC to guide them in their learning of the English language.

Such creation of materials that facilitate language-learning typically involves the use of very large corpora (comparable to the size of the BNC), as well as advanced software and technology. A large amount of money, time and especially expertise in the field of computational linguistics are invested in the development of such language-learning material.[21]


In general, the BNC is useful as a reference source for the purposes of producing and perceiving text. In particular, the BNC can be used as a reference source when studying the use of individual words in various contexts, so that learners become familiar with the different ways to use particular words in suitable contexts.[21]


Bilingual dictionaries[edit]

The BNC was the source of more than 12,000 words and phrases used for the production of a range of bilingual dictionaries in India in 2012, translating 22 local languages into English. This was part of a larger movement to push for improvements in education, the preservation of India's vernacular languages as well as an industry for translation.[23]

Tests and evaluations[edit]


Academic research projects based on the BNC[edit]

  • Collocational Evidence from the British National Corpus[26]

Hoffman & Lehmann (2000) explored the mechanisms behind speakers ability to manipulate their large inventory of collocations which are ready for use and can be easily expanded grammatically or syntactically to adapt to the current speech situation. Word combinations occurring in low frequency were extracted from the BNC to offer some insight into it.[26]

  • Non-sentential Utterances: A Corpus Study[27]

Fernandez & Ginzburg (2002) investigated dialogue which is full of intuitively complete utterances that are not sentential in their outward form. These are typically short answers in response to queries. In their investigation, a corpus-based on the non-sentential utterances (NSU) was carried out on a sub-portion of the BNC to provide a complete and theoretically grounded classification of NSUs in dialogue.[27]

  • A corpus-based EAP course for NNS doctoral students: Moving from available specialized corpora to self-compiled corpora[28]

Lee & Swales (2006) designed an experimental, innovative course in corpus-informed English for Academic Purposes (EAP) for doctoral students at the English Language Institute (ELI) of the University of Michigan in the US.[28]

Participants were given access to specialized corpora of academic writing and speaking, instructed in the tools of the trade (web- & PC-based concordancers) and gradually introduced to the skills needed to best exploit the data and the tools for both directed learning and self-learning. After the induction period, participants began to compile two additional written corpora: one of their own writing (term papers, unedited journal drafts) and one of 'expert' writing, selected from electronic versions of published papers in their own field or subfield. Students were thus able to make comparisons between their own writing and those of more established writers in their field.[28]

Participants used three main corpora as the basis of their investigations: Hyland’s Research Article Corpus, the Michigan Corpus of Academic Spoken English (MICASE) and Academic texts from the BNC.[28]

Natural language processing[edit]


Acclaim for the BNC[edit]

The BNC is widely considered by researchers in the field of computational linguistics and corpus linguistics as a remarkable accomplishment, given its massive collection of words. Since the BNC represents a recognizable effort to collect and subsequently process such a large amount of data, it has become an influential forerunner in the field and a model or exemplary corpus on which the development of later corpora was based.[30]

See also[edit]


  3. ^ a b c d e Leech, Geoffrey (1993). "100 million words of English". English Today 9 (1): 9–15. doi:10.1017/S0266078400006854. 
  4. ^ a b What is the BNC?. Retrieved 12 March 2012.
  6. ^ a b c d e f g Burnard, Lou (2002). "Where did we go wrong? A retrospective look at the British National Corpus". Retrieved 14 March 2012. 
  10. ^ "BNC Sampler: XML edition". 2008. Retrieved 18 March 2012. 
  14. ^ Burnard, Lou (1995). "Users Reference Guide for the British National Corpus". Retrieved 18 March 2012. 
  19. ^ a b Lee, David (2001). GENRES, REGISTERS, TEXT TYPES, DOMAINS, AND STYLES 5 (3). pp. 37–72. Retrieved 15 March 2012. 
  20. ^ a b c Lee, David (2002). "NOTES TO ACCOMPANY THE BNC WORLD EDITION (BIBLIOGRAPHICAL) INDEX". Retrieved 17 March 2012. 
  21. ^ a b c d e f g h i j Aston, Guy, (1998). "Learning English with the British National Corpus". Paper given at 6th Jornada de Corpus, Barcelona: UPF. Retrieved 16 March 2012. 
  22. ^ Minn, Danny; Sano, Hiroshi., Ino, Marie. & Nakamura, Takahiro (2005). "Using the BNC to create and develop educational materials and a website for learners of English". ICAME Journal 29: 99–113. Retrieved 12 March 2012. 
  23. ^ "Bilingual dictionaries to promote India’s mother tongues". Times of Oman. 14 March 2012. Retrieved 17 March 2012. 
  25. ^ Korhonen, Anna (2002). "EVALUATION RESOURCES for English Subcategorization Acquisition Systems". Retrieved 18 March 2012. 
  26. ^ a b Sebastian Hoffmann and Hans-Martin Lehmann (2000) "Collocational Evidence from the British National Corpus", in John M. Kirk, ed. Corpora Galore: Analyses and Techniques in Describing English Amsterdam: Rodopi
  27. ^ a b Fernandez, Raquel; Jonathan Ginzburg (29 June 2002). Non-sentential utterances: A corpus study. 
  28. ^ a b c d Lee, David; John Swales (2006). "A corpus-based EAP course for NNS doctoral students: Moving from available specialized corpora to self-compiled corpora". English for Specific Purposes 25 (1): 56–75. 
  29. ^ a b Minnen, Guido; Carroll, John., Pearce, Darren. (2001). "Applied Morphological Processing of English". Natural Language Engineering 7(3): 207–223. 
  30. ^ Čermák, František (2003). "Today's Corpus Linguistics: Some Open Questions". International Journal of Corpus Linguistics 7 (2): 265–282. 

External links[edit]