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Word-sense disambiguation is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition , it is usually subconscious.
Paraphrase is sense-for-sense translation where the message of the author is kept but the words are not so strictly followed as the sense, which too can be altered or amplified. [10] Imitation is the use of either metaphrase or paraphrase but the translator has the liberty to choose which is appropriate and how the message will be conveyed.
The semantic features of a word can be notated using a binary feature notation common to the framework of componential analysis. [11] A semantic property is specified in square brackets and a plus or minus sign indicates the existence or non-existence of that property. [12] cat is [+animate], [+domesticated], [+feline] puma is [+animate], [− ...
In psychology, meaning-making is the process of how people construe, understand, or make sense of life events, relationships, and the self. [1] The term is widely used in constructivist approaches to counseling psychology and psychotherapy, [2] especially during bereavement in which people attribute some sort of meaning to an experienced death ...
word-sense induction – the task of automatically acquiring the senses of a target word; word-sense disambiguation – the task of automatically associating a sense with a word in context; lexical substitution – the task of replacing a word in context with a lexical substitute; sememe – unit of meaning; linguistics – the scientific study ...
In computational linguistics the Yarowsky algorithm is an unsupervised learning algorithm for word sense disambiguation that uses the "one sense per collocation" and the "one sense per discourse" properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and in a ...
The individual words make sense and are arranged according to proper grammatical rules, yet the result is nonsense. The inspiration for this attempt at creating verbal nonsense came from the idea of contradiction and seemingly irrelevant and/or incompatible characteristics, which conspire to make the phrase meaningless, but are open to ...
The output of a word-sense induction algorithm is a clustering of contexts in which the target word occurs or a clustering of words related to the target word. Three main methods have been proposed in the literature: [1] [2] Context clustering; Word clustering; Co-occurrence graphs