examples, collocations and thesaurus
for learners of English
What is SKELL?
SKELL (Sketch Engine for Language Learning) is a simple tool for students and teachers of English to easily check whether or how a particular phrase or a word is used by real speakers of English.
No registration or payment required. Just type a word and click a button.
All examples, collocations and synonyms were identified automatically by ingenious algorithms and state-of-the-art software analyzing large multi-billion samples of text. No manual work was involved.
SKELL vs. Google Search
SKELL finds good examples of the word or phrase useful for language learners.
Google Search finds web pages with information about the topic specified by the word or phrase.
for learners of English
для изучающих русский язык
apprendenti di italiano
pro studenty češtiny
eesti keele õppijatele
Examples in context – concordance
Use the Examples button to display examples of the word or phrase in context. Type a word (e.g. controllable) or a phrase (e.g. in regard to) and click the Search button, you will get up to 40 example sentences.
How it works
- Base form will find its derived forms too, e.g. searching for mouse will mice. However, searching for mice (plural, not a base form) will only find examples of mice (not mouse).
- Finds all parts of speech.
If you search for work, it will get sentences with work as a noun as well as a verb and both in various word forms (working, worked, works).
More complex searches and other languages
To find examples in other languages, to use more complex criteria, or to search for grammar structures rather than concrete words, use the concordance search in Sketch Engine.
Technology behind SKELL
SKELL is a free simplified interface of Sketch Engine adapted to the needs of learners of English. Sketch Engine is a corpus query and management system holding 400+ corpora in 90+ languages. Sketch Engine is used by linguists, lexicographers, lexicologists and other researchers to learn about how language works. Sketch Engine currently handles about 150 TB (terabytes) of data at an unprecedented speed. It is also designed to handle morphologically rich languages such as Russian, Spanish or Japanese.
All results in SKELL are a product of ingenious algorithms analysing fully automatically large multi-billion word samples of text, called text corpora. There is no manual work involved when generating the collocations, examples or thesaurus results. See more about the English corpus behind the English SKELL.
The text corpora contain a varied collection of texts such as news, academic papers, Wikipedia articles, open-source books, web pages, discussion forums, blogs, etc. to provide a good example of how language is used in everyday, standard, formal and professional context.
About Skell from its users
by James Thomas
on the eflnotes blog by Michael Houston Brown
This service is provided by the copyright holders and contributors “as is” and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall Lexical Computing Ltd. be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.
References to SKELL and versioning
From time to time, the underlying corpus data may change (cleaning, refining etc.). To refer to particular results (using bookmarked URLs for example), also refer to a particular version. The web interface may also change occasionally. Therefore, each SKELL page carries a version in the bottom-right corner, e.g. skell_3_10 v1.6. This refers to the version of the corpus data and the version of the interface respectively.
For reporting issues or writing your feedback, please use the feedback form available on feedback (feedback) in the top-right corner of each page.
If you use results and examples from or links to SKELL, please, cite this paper:
BAISA, Vít a Vít SUCHOMEL. SkELL – Web interface for English Language Learning. In Eighth Workshop on Recent Advances in Slavonic Natural Language Processing. Brno: Tribun EU, 2014, pp. 63-70. ISSN 2336-4289.