SkELL
 | |
|
SkELL search for | |
| Original author(s) | Vít Baisa, Vít Suchomel |
|---|---|
| Developer(s) | Lexical Computing Ltd. |
| Initial release | November 2014 |
| Development status | Active |
| Written in | jQuery, JavaScript |
| Available in | English |
| Type | Language learning |
| License | freeware |
| Website |
skell |
SkELL is an abbreviation of Sketch Engine for Language Learning.[1] It is a web interface for English language learning. The main purpose is to help students and teachers of English language. SkELL has its own corpus[2] that was gathered so that contained texts covering everyday, standard, formal and professional English language.[1] In the corpus, there are more than 60 million sentences and more than one billion words in total.[3]
The SkELL interface provides features such as simple search showing words in context, but the maximum of displayed lines (concordances in fact) is 40. However, you find the frequency of searched query expressed with the number hits per million be located below the search box. The second function is word sketch which enables showing collocates for a given word or words. The last one is named as similar words. It visualises similar words to searched word in a word cloud.
Since 2015, the tool has been available also for Russian language.
Features
SkELL offers three types of searches.
- Examples – searching for words and phrases and their all derived forms
- Word sketch – a simplified version of the original word sketch page
- Similar words – based on the Distributional thesaurus in Sketch Engine, there are not necessarily synonyms
Data
The corpus consists of English Wikipedia (special sorted out 130,000 articles), English collection of Project Gutenberg, a subset from the web corpus enTenTen14,[4] the whole British National Corpus, and free new sources.[1]
Processing the data
After gathering and pre-cleaning (all structures have removed except sentences) data has run through processing pipe: normalization, tokenization, TreeTagger for English, and deduplication. The further process was a compilation of the corpus using manatee indexing library. In the end, all sentences were scored with the GDEX[5] tool.[1]
References
- 1 2 3 4 Baisa, Vít; Suchomel, Vít (2014). "SkELL:Web Interface for English Language Learning" (PDF). Eighth Workshop on Recent Advances in Slavonic Natural Language Processing (NLP Consulting): 63–70.
- ↑ Thomas, James (14 June 2015). "Discovering English with SketchEngine – James Thomas interview". EFL NOTES. Retrieved 21 December 2015.
- ↑ "SkELL". Sketch Engine. Lexical Computing Ltd. Retrieved 21 December 2015.
- ↑ Jakubíček, Miloš.; Kilgarriff, Adam; Kovář, Vojtěch; Rychlý, Pavel; Suchomel, Vít (July 2013). "The tenten corpus family". Seventh International Corpus Linguistics Conference CL (Lancaster University): 125–127.
- ↑ Kilgarriff, A.; Husák, M.; McAdam, K.; Rundell, M.; Rychlý, P. (July 2008). "GDEX: Automatically finding good dictionary examples in a corpus". Proceedings of the XIII EURALEX International Congress (EURALEX): 425–432.