science

Noisy text

Noisy text is a 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 Noisy text rather than just read about it. In short: Noisy text is text with differences between the surface form of a coded representation of the text and the intended, correct, or original text. The noise may be due to typographic errors or colloquialisms always present in natural language and usually lowers the data quality in a way that makes the text less accessible to automated processing by computers, including natural language processing.

Key takeaways

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

Reference excerpt

Noisy text is text with differences between the surface form of a coded representation of the text and the intended, correct, or original text. The noise may be due to typographic errors or colloquialisms always present in natural language and usually lowers the data quality in a way that makes the text less accessible to automated processing by computers, including natural language processing. The noise may also have been introduced through an extraction process (e.g., transcription or OCR) from media other than original electronic texts. Language usage over computer mediated discourses, like chats, emails and SMS texts, significantly differs from the standard form of the language. An urge towards shorter message length facilitating faster typing and the need for semantic clarity, shape the structure of this text used in such discourses. Various business analysts estimate that unstructured data constitutes around 80% of the whole enterprise data. A great proportion of this data comprises chat transcripts, emails and other informal and semi-formal internal and external communications. Usually such text is meant for human consumption, but—given the amount of data—manual processing and evaluation of those resources is not practically feasible anymore. This raises the need for robust text mining methods.

Techniques for noise reduction

The use of spell checkers and grammar checkers can reduce the amount of noise in typed text. Many word processors include this in the editing tool. Online, Google Search includes a search term suggestion engine to guide users when they make mistakes with their queries.

See also Data corruption Jargon Leet speak Natural language understanding Noisy channel

References

Worked examples

Example 1 — a first encounter with Noisy text

Start with the simplest possible case. Write down what Noisy text claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In 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 Noisy text 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 Noisy text 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 Noisy text

In research: Noisy text appears in 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 Noisy text 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: Noisy text is common in secondary-school and first-year university syllabi. It links to neighbouring topics Coding theory, so understanding it makes those chapters shorter.
In everyday life: Look for Noisy text 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 Noisy text in 20 minutes

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

Frequently asked questions

What is Noisy text in simple terms?

Why does Noisy text matter?

Because it connects several 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 Noisy text?

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 Noisy text.