ArticleslgStudy

computer science

Name resolution (semantics and text extraction)

Name resolution (semantics and text extraction) 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 Name resolution (semantics and text extraction) rather than just read about it. In short: In semantics and text extraction, name resolution refers to the ability of text mining software to determine which actual person, actor, or object a particular use of a name refers to. It can also be referred to as entity resolution.

Key takeaways

  • Name resolution (semantics and text extraction) 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 Name resolution (semantics and text extraction) to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Name resolution (semantics and text extraction) from memory before moving on to harder problems.

Reference excerpt

In semantics and text extraction, name resolution refers to the ability of text mining software to determine which actual person, actor, or object a particular use of a name refers to. It can also be referred to as entity resolution.

Name resolution in simple text For example, in the text mining field, software frequently needs to interpret the following text:

John gave Edward the book. He then stood up and called to John to come back into the room. In these sentences, the software must determine whether the pronoun "he" refers to "John", or "Edward" from the first sentence. The software must also determine whether the "John" referred to in the second sentence is the same as the "John" in the first sentence, or a third person whose name also happens to be "John". Such examples apply to almost all languages, and not only English.

Name resolution across documents Frequently, this type of name resolution is also used across documents, for example to determine whether the "George Bush" referenced in an old newspaper article as President of the United States (George H. W. Bush) is the same person as the "George Bush" mentioned in a separate news article years later about a man who is running for President (George W. Bush.) Because many people may have the same name, analysts and software must take into account substantially more information than only a name to determine whether two identical references ("George Bush") actually refer to the same specific entity or person. Name/entity resolution in text extraction and semantics is a notoriously difficult problem, in part because in many cases there is not sufficient information to make an accurate determination. Numerous partial solutions exist that rely on specific contextual clues found in the data, but there is no currently known general solution. The problem is sometimes referred to as name disambiguation and, for digital libraries, author disambiguation. For examples of software that might provide name resolution benefits, see also:

AeroText AlchemyAPI Attensity Autonomy Basis Technology Dandelion API, providing a customizable approach for name resolution using an internal knowledge graph (built on Wikipedia, DBpedia and other sources) DBpedia Spotlight, providing a simple approach for name resolution using DBpedia and Wikipedia NetOwl

See also Identity resolution Named entity recognition Naming collision Anaphor resolution

References

Worked examples

Example 1 — a first encounter with Name resolution (semantics and text extraction)

Start with the simplest possible case. Write down what Name resolution (semantics and text extraction) 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 Name resolution (semantics and text extraction) 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 Name resolution (semantics and text extraction) 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 Name resolution (semantics and text extraction)

In research
Name resolution (semantics and text extraction) 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 Name resolution (semantics and text extraction) 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
Name resolution (semantics and text extraction) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computational linguistics, Tasks of natural language processing, so understanding it makes those chapters shorter.
In everyday life
Look for Name resolution (semantics and text extraction) 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.
Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Name resolution (semantics and text extraction)” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Name resolution (semantics and text extraction) in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Name resolution (semantics and text extraction) 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 Name resolution (semantics and text extraction) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Name resolution (semantics and text extraction) in simple terms?

In semantics and text extraction, name resolution refers to the ability of text mining software to determine which actual person, actor, or object a particular use of a name refers to. It can also be referred to as entity resolution.

Why does Name resolution (semantics and text extraction) 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 Name resolution (semantics and text extraction)?

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 Name resolution (semantics and text extraction).

Tags

  • Computational linguistics
  • Tasks of natural language processing

Keep exploring