science

Meaning–text theory

Meaning–text theory 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 Meaning–text theory rather than just read about it. In short: Meaning–text theory (MTT) is a theoretical linguistic framework, first put forward in Moscow by Aleksandr Žolkovskij and Igor Mel’čuk, for the construction of models of natural language. The theory provides a large and elaborate basis for linguistic description and, due to its formal character, lends itself particularly well to computer applications, including machine translation, phraseology, and lexicography.

Key takeaways

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

Reference excerpt

Meaning–text theory (MTT) is a theoretical linguistic framework, first put forward in Moscow by Aleksandr Žolkovskij and Igor Mel’čuk, for the construction of models of natural language. The theory provides a large and elaborate basis for linguistic description and, due to its formal character, lends itself particularly well to computer applications, including machine translation, phraseology, and lexicography.

Levels of representation Linguistic models in meaning–text theory operate on the principle that language consists of a mapping from the content or meaning (semantics) of an utterance to its form or text (phonetics). Intermediate between these poles are additional levels of representation at the syntactic and morphological levels.

Representations at the different levels are mapped, in sequence, from the unordered network of the semantic representation (SemR) through the dependency tree-structures of the syntactic representation (SyntR) to a linearized chain of morphemes of the morphological representation (MorphR) and, ultimately, the temporally-ordered string of phones of the phonetic representation (PhonR) (not generally addressed in work in this theory). The relationships between representations on the different levels are considered to be translations or mappings, rather than transformations, and are mediated by sets of rules, called "components", which ensure the appropriate, language-specific transitions between levels.

Semantic representation Semantic representations (SemR) in meaning–text theory consist primarily of a web-like semantic structure (SemS) which combines with other semantic-level structures (most notably the semantic-communicative structure [SemCommS], which represents what is commonly referred to as "information structure" in other frameworks). The SemS itself consists of a network of predications, represented as nodes with arrows running from predicate nodes to argument node(s). Arguments can be shared by multiple predicates, and predicates can themselves be arguments of other predicates. Nodes generally correspond to lexical and grammatical meanings as these are directly expressed by items in the lexicon or by inflectional means, but the theory allows the option of decomposing meanings into more fine-grained representation via processes of semantic paraphrasing, which are also key to dealing with synonymy and translation-equivalencies between languages. SemRs are mapped onto the next level of representation, the deep-syntactic representation, by the rules of the semantic component, which allow for a one to many relationship between levels (that is, one SemR can potentially be expressed by a variety of syntactic structures, depending on lexical choice, the complexity of the SemR, etc.). The structural description and the (semi-) automatic generation of SemR are subject to research. Here the decomposition takes advantage of the semantic primes of the natural semantic metalanguage to determine a termination criterion of the decomposition.

Syntactic representation Syntactic representations (SyntR) in meaning–text theory are implemented using dependency trees, which constitute the syntactic structure (SyntS). SyntS is accompanied by various other types of structure, most notably the syntactic communicative structure and the anaphoric structure. There are two levels of syntax in meaning–text theory, the deep syntactic representation (DSyntR) and the surface syntactic representation (SSyntR). A good overview of meaning–text theory syntax, including its descriptive application, can be found in Mel’čuk (1988). A comprehensive model of English surface syntax is presented in Mel’čuk & Pertsov (1987). The deep syntactic representation (DSyntR) is related directly to SemS and seeks to capture the "universal" aspects of the syntactic structure. Trees at this level represent dependency relations between lexemes (or between lexemes and a limited inventory of abstract entities such as lexical functions). Deep syntactic relations between lexemes at DSyntR are restricted to a universal inventory of a dozen or syntactic relations including seven ranked actantial (argument) relations, the modificative relation, and the coordinative relation. Lexemes with purely grammatical function such as lexically-governed prepositions are not included at this level of representation; values of inflectional categories that are derived from SemR but implemented by the morphology are represented as subscripts on the relevant lexical nodes that they bear on. DSyntR is mapped onto the next level of representation by rules of the deep-syntactic component. The surface-syntactic representation (SSyntR) represents the language-specific syntactic structure of an utterance and includes nodes for all the lexical items (including those with purely grammatical function) in the sentence. Syntactic relations between lexical items at this level are not restricted and are considered to be completely language-specific, although many are believed to be similar (or at least isomorphic) across languages. SSyntR is mapped onto the next level of representation by rules of the surface-syntactic component.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Meaning–text theory

Start with the simplest possible case. Write down what Meaning–text theory 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 Meaning–text theory 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 Meaning–text theory 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 Meaning–text theory

In research: Meaning–text theory 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 Meaning–text theory 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: Meaning–text theory is common in secondary-school and first-year university syllabi. It links to neighbouring topics Dependency grammar, Lexicography, Linguistic morphology, so understanding it makes those chapters shorter.
In everyday life: Look for Meaning–text theory 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 Meaning–text theory in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Meaning–text theory 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 Meaning–text theory out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Meaning–text theory in simple terms?

Why does Meaning–text theory 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 Meaning–text theory?

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 Meaning–text theory.