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Text corpus

Text corpus is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Text corpus rather than just read about it. In short: In linguistics and natural language processing, a corpus (pl.: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated. Annotated, they have been used in corpus linguistics for statistical hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory.

Key takeaways

  • Text corpus belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Text corpus to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Text corpus from memory before moving on to harder problems.

Reference excerpt

In linguistics and natural language processing, a corpus (pl.: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated. Annotated, they have been used in corpus linguistics for statistical hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory.

Overview A corpus may contain texts in a single language (monolingual corpus) or text data in multiple languages (multilingual corpus). In order to make the corpora more useful for doing linguistic research, they are often subjected to a process known as annotation. An example of annotating a corpus is part-of-speech tagging, or POS-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 the lemma (base) form of each word. When the language of the corpus is not a working language of the researchers who use it, interlinear glossing is used to make the annotation bilingual. Some corpora have further structured levels of analysis applied. In particular, smaller corpora may be fully parsed. Such corpora are usually called Treebanks or Parsed Corpora. The difficulty of ensuring that the entire corpus is completely and consistently annotated means that these corpora are usually smaller, containing around one to three million words. Other levels of linguistic structured analysis are possible, including annotations for morphology, semantics and pragmatics.

Applications Corpora are the main knowledge base in corpus linguistics. Other notable areas of application include:

Language technology, natural language processing, computational linguistics The analysis and processing of various types of corpora are also the subject of much work in computational linguistics, speech recognition and machine translation, where they are often used to create hidden Markov models for part of speech tagging and other purposes. Corpora and frequency lists derived from them are useful for language teaching. Corpora can be considered as a type of foreign language writing 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 sentence formation in the target language, enabling effective writing. Machine translation Multilingual corpora that have been specially formatted for side-by-side comparison are called aligned parallel corpora. There are two main types of parallel corpora which contain texts in two languages. In a translation corpus, the texts in one language are translations of texts in the other language. In a comparable corpus, the texts are of the same kind and cover the same content, but they are not translations of each other. To exploit a parallel text, some kind of text alignment identifying equivalent text segments (phrases or sentences) is a prerequisite for analysis. Machine translation algorithms for translating between two languages are often trained using parallel fragments comprising a first-language corpus and a second-language corpus, which is an element-for-element translation of the first-language corpus. Philologies Text corpora are also used in the study of historical documents, for example in attempts to decipher ancient scripts, or in Biblical scholarship. Some archaeological corpora can be of such short duration that they provide a snapshot in time. One of the shortest corpora in time may be the 15–30 year Amarna letters texts (1350 BC). The corpus of an ancient city, (for example the "Kültepe Texts" of Turkey), may go through a series of corpora, determined by their find site dates.

Some notable text corpora

See also Concordance Corpus linguistics Culturomics Distributional–relational database Linguistic Data Consortium Natural language processing Natural Language Toolkit Parallel text Speech corpus Translation memory Treebank Zipf's law

References

External links ACL SIGLEX Resource Links: Text Corpora Archived 2013-08-13 at the Wayback Machine Developing Linguistic Corpora: a Guide to Good Practice Free samples (not free), web-based corpora (45-425 million words each): American (COCA, COHA, TIME), British (BNC), Spanish, Portuguese Intercorp Building synchronous parallel corpora of the languages taught at the Faculty of Arts of Charles University. Sketch Engine: Open corpora with free access TS Corpus – A Turkish Corpus freely available for academic research. Turkish National Corpus – A general-purpose corpus for contemporary Turkish Archived 2 April 2015 at the Wayback Machine Corpus of Political Speeches, Free access to political speeches by American and Chinese politicians, developed by Hong Kong Baptist University Library Russian National Corpus

Worked examples

Example 1 — a first encounter with Text corpus

Start with the simplest possible case. Write down what Text corpus claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Text corpus before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Text corpus ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Text corpus

In research
Text corpus appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Text corpus in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Text corpus is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computational linguistics, Corpus linguistics, Discourse analysis, so understanding it makes those chapters shorter.
In everyday life
Look for Text corpus outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Text corpus in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Text corpus means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Text corpus out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Text corpus in simple terms?

In linguistics and natural language processing, a corpus (pl.: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated. Annotated, they have been used in corpus linguistics for statistical hypothesis testing, c…

Why does Text corpus matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Text corpus?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Text corpus.

Tags

  • Computational linguistics
  • Corpus linguistics
  • Discourse analysis
  • Works based on multiple works

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