computer science

Machine-readable dictionary

Machine-readable dictionary 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 Machine-readable dictionary rather than just read about it. In short: Machine-readable dictionary (MRD) is a dictionary stored as machine-readable data instead of being printed on paper. It is an electronic dictionary and lexical database.

Key takeaways

Machine-readable dictionary 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 Machine-readable dictionary to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Machine-readable dictionary from memory before moving on to harder problems.

Reference excerpt

Machine-readable dictionary (MRD) is a dictionary stored as machine-readable data instead of being printed on paper. It is an electronic dictionary and lexical database. A machine-readable dictionary is a dictionary in an electronic form that can be loaded in a database and can be queried via application software. It may be a single language explanatory dictionary or a multi-language dictionary to support translations between two or more languages or a combination of both. Translation software between multiple languages usually apply bidirectional dictionaries. An MRD may be a dictionary with a proprietary structure that is queried by dedicated software (for example online via internet) or it can be a dictionary that has an open structure and is available for loading in computer databases and thus can be used via various software applications. Conventional dictionaries contain a lemma with various descriptions. A machine-readable dictionary may have additional capabilities and is therefore sometimes called a smart dictionary. An example of a smart dictionary is the Open Source Gellish English dictionary. The term dictionary is also used to refer to an electronic vocabulary or lexicon as used for example in spelling checkers. If dictionaries are arranged in a subtype-supertype hierarchy of concepts (or terms) then it is called a taxonomy. If it also contains other relations between the concepts, then it is called an ontology. Search engines may use either a vocabulary, a taxonomy or an ontology to optimise the search results. Specialised electronic dictionaries are morphological dictionaries or syntactic dictionaries. The term MRD is often contrasted with NLP dictionary, in the sense that an MRD is the electronic form of a dictionary which was printed before on paper. Although being both used by programs, in contrast, the term NLP dictionary is preferred when the dictionary was built from scratch with NLP in mind. An ISO standard for MRD and NLP is able to represent both structures and is called Lexical Markup Framework.

History The first widely distributed MRDs were the Merriam-Webster Seventh Collegiate (W7) and the Merriam-Webster New Pocket Dictionary (MPD). Both were produced by a government-funded project at System Development Corporation under the direction of John Olney. They were manually keyboarded as no typesetting tapes of either book were available. Originally each was distributed on multiple reels of magnetic tape as card images with each separate word of each definition on a separate punch card with numerous special codes indicating the details of its usage in the printed dictionary. Olney outlined a grand plan for the analysis of the definitions in the dictionary, but his project expired before the analysis could be carried out. Robert Amsler at the University of Texas at Austin resumed the analysis and completed a taxonomic description of the Pocket Dictionary under National Science Foundation funding, however his project expired before the taxonomic data could be distributed. Roy Byrd et al. at IBM Yorktown Heights resumed analysis of the Webster's Seventh Collegiate following Amsler's work. Finally, in the 1980s starting with initial support from Bellcore and later funded by various U.S. federal agencies, including NSF, ARDA, DARPA, DTO, and REFLEX, George Armitage Miller and Christiane Fellbaum at Princeton University completed the creation and wide distribution of a dictionary and its taxonomy in the WordNet project, which today stands as the most widely distributed computational lexicology resource.

References

Worked examples

Example 1 — a first encounter with Machine-readable dictionary

Start with the simplest possible case. Write down what Machine-readable dictionary 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 Machine-readable dictionary 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 Machine-readable dictionary 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 Machine-readable dictionary

In research: Machine-readable dictionary 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 Machine-readable dictionary 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: Machine-readable dictionary is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computational linguistics, Dictionaries by type, Lexicography, so understanding it makes those chapters shorter.
In everyday life: Look for Machine-readable dictionary 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 Machine-readable dictionary in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Machine-readable dictionary 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 Machine-readable dictionary out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Machine-readable dictionary in simple terms?

Machine-readable dictionary (MRD) is a dictionary stored as machine-readable data instead of being printed on paper. It is an electronic dictionary and lexical database.

Why does Machine-readable dictionary 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 Machine-readable dictionary?

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 Machine-readable dictionary.