computer science

Reification (information retrieval)

Reification (information retrieval) 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 Reification (information retrieval) rather than just read about it. In short: In information retrieval and natural language processing reification is the process by which an abstract idea about a person, place or thing, is turned into an explicit data model or other object created in a programming language, such as a feature set of demographic or psychographic attributes or both. By means of reification, something that was previously implicit, unexpressed, and possibly inexpressible is explic…

Key takeaways

Reification (information retrieval) 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 Reification (information retrieval) to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Reification (information retrieval) from memory before moving on to harder problems.

Reference excerpt

In information retrieval and natural language processing reification is the process by which an abstract idea about a person, place or thing, is turned into an explicit data model or other object created in a programming language, such as a feature set of demographic or psychographic attributes or both. By means of reification, something that was previously implicit, unexpressed, and possibly inexpressible is explicitly formulated and made available to conceptual (logical or computational) manipulation. The process by which a natural language statement is transformed so actions and events in it become quantifiable variables is semantic parsing. For example "John chased the duck furiously" can be transformed into something like

(Exists e)(chasing(e) & past_tense(e) & actor(e,John) & furiously(e) & patient(e,duck)). Another example would be "Sally said John is mean", which could be expressed as something like

(Exists u,v)(saying(u) & past_tense(u) & actor(u,Sally) & that(u,v) & is(v) & actor(v,John) & mean(v)). Such formal meaning representations allow one to use the tools of classical first-order predicate calculus even for statements which, due to their use of tense, modality, adverbial constructions, propositional arguments (e.g. "Sally said that X"), etc., would have seemed intractable. This is an advantage because predicate calculus is better understood and simpler than the more complex alternatives (higher-order logics, modal logics, temporal logics, etc.), and there exist better automated tools (e.g. automated theorem provers and model checkers) for manipulating it. Meaning representations can be used for other purposes besides the application of first-order logic; one example is the automatic discovery of synonymous phrases. The meaning representations are sometimes called quasi-logical forms, and the existential variables are sometimes treated as Skolem constants. Not all natural language constructs admit a uniform translation to first order logic. See donkey sentence for examples and a discussion.

See also Drinker paradox Nonfirstorderizability Reification (computer science) Reification (fallacy) Reification (knowledge representation)

References

Worked examples

Example 1 — a first encounter with Reification (information retrieval)

Start with the simplest possible case. Write down what Reification (information retrieval) 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 Reification (information retrieval) 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 Reification (information retrieval) 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 Reification (information retrieval)

In research: Reification (information retrieval) 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 Reification (information retrieval) 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: Reification (information retrieval) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computational linguistics, so understanding it makes those chapters shorter.
In everyday life: Look for Reification (information retrieval) 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 Reification (information retrieval) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Reification (information retrieval) 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 Reification (information retrieval) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Reification (information retrieval) in simple terms?

Why does Reification (information retrieval) 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 Reification (information retrieval)?

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 Reification (information retrieval).