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Inlinguistics, a corpus(plural corpora) or text corpusis a large and structured set of texts(nowadays usually electronically stored and processed). They are used to do statistical analysisandhypothesis testing, checking occurrences or validating linguistic rules within a specificlanguage territory.

A corpus may contain texts in a single language (monolingual corpus) or text data in multiplelanguages (multilingual corpus). Multilingual corpora that have been specially formatted forside-by-side comparison are called aligned parallel corpora.

In order to make the corpora more useful for doing linguistic research, they are often subjected toa process known asannotation. An example of annotating a corpus ispart-of-speech tagging,orPOS-tagging, in which information about each word's part of speech (verb, noun, adjective,etc.) is added to the corpus in the form of tags. Another example is indicating thelemma (base)form of each word. When the language of the corpus is not a working language of the researcherswho use it,interlinear glossing is used to make the annotation bilingual.

Some corpora have furtherstructuredlevels of analysis applied. In particular, a number ofsmaller corpora may be fullyparsed. Such corpora are usually calledTreebanks orParsed

Corpora. The difficulty of ensuring that the entire corpus is completely and consistentlyannotated means that these corpora are usually smaller, containing around one to three millionwords. Other levels of linguistic structured analysis are possible, including annotationsformorphology,semantics andpragmatics.

Corpora are the main knowledge base incorpus linguistics. The analysis and processing ofvarious types of corpora are also the subject of much work incomputational linguistics,speechrecognition andmachine translation, where they are often used to createhidden Markovmodels for part of speech tagging and other purposes. Corpora andfrequency lists derived fromthem are useful forlanguage teaching.Corpora can be considered as a type offoreign languagewriting aid as the contextualised grammatical knowledge acquired by non-native language users

through exposure to authentic texts in corpora allows learners to grasp the manner of sentenceformation in the target language, enabling effective writing.[1]

Definition of hyponymin Englishhyponym

Pronunciation:/hp()nm/

noun

a word of more specific meaning than a general or superordinate term applicable to it. Forexample,spoonis a hyponym of cutlery.Contrasted withHYPERNYM.

meronym

Pronunciation:/mrnm/

noun

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Linguistics

a term which denotes part of something but which is used to refer to the whole of it,

e.g.faceswhen used to meanpeopleinI see several familiar faces present.

---------------

A snippet is a piece of text that you would like to insert in your document. It can include code to

run at insertion time, variables (like selected text), tab stops/placeholders for missing information

(which you can tab through after insertion) and perform transformations on the data which you

enter in the placeholders.

In the simplest case, you can use snippets to insert text that you do not want to type again andagain, either because you type it a lot, or because the actual text to insert is hard to remember(like your bank account details or the HTML entities for the Apple modifier keys).

If you use snippets to insert plain text there is only one thing you should be aware of: $ and ` arereserved characters. So if you want to insert one of these, prefix it with an escape (i.e. \$). Anescape not followed by one of these two characters (or followed by another escape) will beinserted as a literal character.

You can insert the value of avariableby prefixing the name of the variable with $. All the

normal dynamic variables are supported, the most useful probably

being TM_SELECTED_TEXT. If for example we want to create a snippet which wraps the

selection in a LaTeX \textbf command, we can make a snippet which is:

contain or include; "This new system subsumes the old one"

englober,contenir,subsumer

v.consider (an instance of something) as part of a general rule or principle

In general, ontology (pronounced ahn-TAH-luh-djee) is the study or concern about what kinds of things exist -

whatentitiesthere are in the universe. It derives from the Greek onto(being) and logia(written or spoken discourse). It is

a branch ofmetaphysics, the study of first principles or the essence of things.

In information technology, ontology is the working model of entities and interactions in some particular domain of

knowledge or practices, such as electronic commerce or "the activity of planning." In artificial intelligence ( AI), an

ontology is, according to Tom Gruber, an AI specialist at Stanford University, "the specification of conceptualizations,

used to help programs and humans share knowledge." In this usage, an ontology is a set of concepts - such as things,

events, and relations - that are specified in some way (such as specific natural language) in order to create an agreed-

upon vocabulary for exchanging information.

This was last updated in September 2005

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the chi-squared distribution(alsochi-squareor -distribution) with kdegrees of freedomis the distribution of a sum of the

squares of kindependentstandard normalrandom variables. It is one of the most widely usedprobability distributionsininferential

statistics,e.g., inhypothesis testingor in construction ofconfidence intervals.[2][3][4][5]

When there is a need to contrast it with

thenoncentral chi-squared distribution,this distribution is sometimes called the central chi-squared distribution.

The chi-squared distribution is used in the commonchi-squared testsforgoodness of fitof an observed distribution to a theoretical

one, theindependenceof two criteria of classification ofqualitative data,and inconfidence intervalestimation for a

populationstandard deviationof a normal distribution from a sample standard deviation. Many other statistical tests also use thisdistribution, likeFriedman's analysis of variance by ranks.

Inmachine learning,support vector machines(SVMs, also support vector networks[1]

)aresupervised learningmodels with

associated learningalgorithmsthat analyze data and recognize patterns, used forclassificationandregression analysis.Given a set

of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns

new examples into one category or the other, making it a non-probabilisticbinarylinear classifier.An SVM model is a representation

of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as

wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side

of the gap they fall on.

In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called thekernel

trick,implicitly mapping their inputs into high-dimensional feature spaces.

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