Natural Language Expression
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- AKA: Linguistic Statement.
- It can (typically) be instantiated as a Linguistic Expression Mention.
- It can (typically) represent some Meaning (e.g. an ambiguous linguistic expression).
- It can (typically) be produced by a Linguistic Agent (with a Linguistic Expression Generation Capability).
- It can (typically) be processed by a Linguistic Agent (with a Linguistic Expression Processing Capability).
- It can (typically) be composed of Linguistic Components.
- It can (often) be a part of an Linguistic Expression Sequence (e.g. a speech or text document).
- It can (often) be bounded by a Linguistic Expression Terminator (such as a pause or a punctuation mark).
- It can range from being a Linguistic Sentence, a Linguistic Clause, a Linguistic Phrase, a Linguistic Word Form.
- It can range from being a Spoken Expression(utterance), a Signed Expression or a Written Expression.
- It can range from being a Grammatically Correct Expression to being a Grammatically Incorrect Expression.
- It can range from being a Formal Linguistic Utterance to being an Informal Linguistic Utterance.
- It can range from being a Complex Linguistic Utterance to being a Simple Linguistic Utterance.
- It can range from being a Referring Expression to being a Nonreferring Expression.
- It can range from being a Truthful Linguistic Expression to being a False Linguistic Expression.
- It can be the Input to a Linguistic Analysis Task (a linguistic processing task).
- It can be described by: a Linguistic Morphological Theory; a Linguistic Syntactic Theory; a Linguistic Semantic Theory; and a Linguistic Pragmatic Theory.
- It can range from being: an Objective Linguistic Expression (e.g. a Factual Linguistic Expression) to being a Subjective Linguistic Expression (e.g. a Negative Linguistic Expression, Positive Linguistic Expression).
- It can be a Lexically Ambiguous Linguistic Expression (Cruse, 1986) - Lexical Semantics
- It can be a Structurally Ambiguous Linguistic Expression (Church & Patil, 1982) - Coping with Syntact ambiguity
- It can be a Socially Contextual Linguistic Expression (Leckie-Tarry & Birch, 1995) - Language Context
- It can be a Metaphoric Linguistic Expression (Lakoff & Johnson, 1980) - Metaphors we live by
- “dha <pause>”
- “Hey, Bill1”, "I walked home”, “I walked and walked.”, “I, you know”, ...
- “Well, well, well”.
- “Happy Birthday!”.
- “I, you know, <pause> took off.<pause> (two utterances, one sentence).
- “Colorless green ideas sleep furiously.”, a Grammatical Sentence.
- "Yesterday was a sad day. All were remorseful." (two utterances, two sentences).
- "Yo went hier.", a Sentence derived from three Natural Languages (Spanish, English, French).
- one within a Summary.
- The character sequence
010AE497h, because it is not a Natural Language Word.
- A Speech, because it is composed of several utterances (a Spoken Linguistic Expression Sequence).
- A Text Document, because it is composed of several utterances (a Written Linguistic Expression Sequence).
- a Non-Linguistic Expression.
- See: Natural Language, Utterance, Natural Language Syntactic Theory, Natural Language Semantic Theory.
- (Jonathan, 2004) ⇒ Jonathan Lawry. (2004). “A framework for linguistic modelling." Artificial Intelligence, 155,(1)
- A new framework for linguistic reasoning is proposed based on a random set model of the degree of appropriateness of a label. Labels are assumed to be chosen from a finite predefined set of labels and the set of appropriate labels for a value is defined as a random set-valued function from a population of individuals into the set of subsets of labels. Appropriateness degrees are then evaluated relative to the distribution on this random set where the appropriateness degree of a label corresponds to the probability that it is contained in the set of appropriate labels. This interpretation is referred to as label semantics. A natural calculus for appropriateness degrees is described which is weakly functional while taking into account the logical structure of expressions. Given this framework it is shown that a bayesian approach can be adopted in order to infer probability distributions on the underlying variable given constraints both in the form of linguistic expressions and mass assignments. In addition, two conditional measures are introduced for evaluating the appropriateness of a linguistic expression given other linguistic information.