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Shallow semantic parsing is concerned with identifying entities in an utterance and labelling them with the roles they play. Shallow semantic parsing is sometimes known as slot-filling or frame semantic parsing, since its theoretical basis comes from frame semantics, wherein a word evokes a frame of related concepts and roles.
Deep linguistic processing is a natural language processing framework which draws on theoretical and descriptive linguistics.It models language predominantly by way of theoretical syntactic/semantic theory (e.g. CCG, HPSG, LFG, TAG, the Prague School).
In natural language processing, semantic role labeling (also called shallow semantic parsing or slot-filling) is the process that assigns labels to words or phrases in a sentence that indicates their semantic role in the sentence, such as that of an agent, goal, or result. It serves to find the meaning of the sentence.
In formal semantics, semantic interpretation is viewed as a mapping from syntactic structures to denotations.There are several formal views of the syntax–semantics interface which differ in what they take to be the inputs and outputs of this mapping.
Combinatory categorial grammar (CCG) is an efficiently parsable, yet linguistically expressive grammar formalism.It has a transparent interface between surface syntax and underlying semantic representation, including predicate–argument structure, quantification and information structure.
A notable example of deep semantic annotation is the Groningen Meaning Bank, developed at the University of Groningen and annotated using Discourse Representation Theory. An example of a shallow semantic treebank is PropBank , which provides annotation of verbal propositions and their arguments, without attempting to represent every word in the ...
That is, rather than functioning as a pure one-way pipeline from the lexer to the parser, there is a backchannel from semantic analysis back to the lexer. This mixing of parsing and semantic analysis is generally regarded as inelegant, which is why it is called a "hack".
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]