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Glossary
Words in italics refer to terms explained elsewhere on this page.
* = Links to web-pages made to supplement the book "Corpus Linguistics" by Tony McEnery and Andrew Wilson.
+ = Links to Systematic Dictionary of Corpus Linguistics made in Lithuania.
- annotation: (linguistic) information, such as
POS tags or syntactic parsing that is added to a text/corpus.
(*) (+)
(external link)
- annotate: provide text with annotation.
- colligation: collocation patterns based on syntactic groups rather than individual words. (Barnbrook 1996)
- collocation: patterns of words appearing together. (*) (+)
- collocate: to appear together, or words that appear together.
(In the collocations 'apple tree', 'apple pie', and 'Adam's apple',
'apple' collocates with 'tree', 'pie', and 'Adam's'. They are
collocates.)
- compile: collect and put together (for example, texts for a corpus).
- concordance: A word/phrase and its surrounding context. Usually printed as a KWIC display. (+)
- context: here usually the words surrounding a hit.
- corpus (pl. corpora or corpuses): a collection of text, now usually in machine-readable form and compiled to be representative of a particular kind of language and provided with some kind of annotation. (*) (+)
- encoding: annotation (+) (*)
- hit: When your search string is found in the corpus, it is referred to as a hit or match.
- KWIC (Key-Word In Context): a form of concordance where the hit is shown with a certain amount of context, often presented with the hit in the centre of the page. (example)
- lemma: the set of different forms of a word, such as the inflected forms of a verb. Ex. 'sing', 'sang', 'sung' are one lemma, 'boy', 'boys' another.
lemmatisation: the process or result of dividing a text into lemmas.+- mark-up: codes used to provide information about a text, such as POS tags, SGML codes etc
- match: When your search string is found in the corpus, it is referred to as a match or hit.
- natural language: term used for human language, as opposed to artificial languages used for, for example, computer programming and formal logic.
NLP Natural Language Processing- parsing: the process or result of making a syntactic analysis (*) (+)
parser: tool (often automatic or semi-automatic computer program) used for parsing. ( General Description of Parsers - external link)
parsed corpus: a corpus that have been syntactically analysed and provided with annotation representing the analysis.- part-of-speech (POS): word class, such as verb, noun, adjective.
part-of-speech tagging: assigning part-of-speech tags to a text. (*)- SGML: (Standard Generalized Mark-up Language) mark-up system used for electronic text. (external link)
- string: combination of letters/characters.
- tag: a label associated with a word (or other unit) providing information about the word, or the process of assigning tags. See annotation. Ex: 'run' can be tagged as a noun (run_N) or verb (run_V).
tagging: the process or result of assigning tags.
tag-set: set of tags used for annotation. (external link)- TEI: (Text Encoding Initiative) International project set up "to develop guidelines for the preparation and interchange of electronic texts...". Uses SGML as starting-point.
- thin: remove certain hits, either automatically or manually.
thinning: the process or result of removing certain hits, either by selecting the desired ones, selecting the ones to discard or by selecting/discarding a set amount of hits.- token: individual word. Compare type.
tokenisation: the process or result of dividing a text or list of words into tokens.- treebank: term sometimes used for parsed corpora.
- type: wordform. "I see a cat and a dog" contains seven tokens but only six types (the type 'a' occurrs twice).
* Links to web-pages made to supplement the book "Corpus Linguistics" by Tony McEnery and Andrew Wilson.
+ Links to Systematic Dictionary of Corpus Linguistics made in Lithuania.
- colligation: collocation patterns based on syntactic groups rather than individual words. (Barnbrook 1996)
Are there words you miss, that you think should be included in the list above? Please let us know and we will try to update the glossary. Comments and suggestions are also welcome. Thank you!