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A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples. Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing [2] and word-sense disambiguation. [3]
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], [2] which represents what is commonly referred to as "information structure" in other frameworks).
In formal linguistics, discourse representation theory (DRT) is a framework for exploring meaning under a formal semantics approach. One of the main differences between DRT-style approaches and traditional Montagovian approaches is that DRT includes a level of abstract mental representations (discourse representation structures, DRS) within its formalism, which gives it an intrinsic ability to ...
Semantics studies meaning in language, which is limited to the meaning of linguistic expressions. It concerns how signs are interpreted and what information they contain. An example is the meaning of words provided in dictionary definitions by giving synonymous expressions or paraphrases, like defining the meaning of the term ram as adult male sheep. [22]
Social semiotics (also social semantics) [1] is a branch of the field of semiotics which investigates human signifying practices in specific social and cultural circumstances, and which tries to explain meaning-making as a social practice.
The semantic gap characterizes the difference between two descriptions of an object by different linguistic representations, for instance languages or symbols. According to Andreas M. Hein, the semantic gap can be defined as "the difference in meaning between constructs formed within different representation systems". [ 1 ]
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
Semantic technologies are "meaning-centered". They involve but are not limited to the following areas of application: encoding/decoding of semantic representation, knowledge graphs of entities and their interrelationships, auto-recognition of topics and concepts, information and meaning extraction, semantic data integration, and; taxonomies ...