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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.
A Karaka Based Approach to Parsing of Indian Languages; Google AI Blog: SLING: A Natural Language Frame Semantic Parser; DFG Collaborative Research Centre 991: The Structure of Representations in Language, Cognition, and Science; Frame Semantics for Text Understanding by Charles J. Fillmore and Collin F. Baker, 2001
A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation . It is a directed or undirected graph consisting of vertices , which represent concepts , and edges , which represent semantic relations between concepts , [ 1 ...
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.
FrameNet is a group of online lexical databases based upon the theory of meaning known as Frame semantics, developed by linguist Charles J. Fillmore.The project's fundamental notion is simple: most words' meanings may be best understood in terms of a semantic frame, which is a description of a certain kind of event, connection, or item and its actors.
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans.
PropBank differs from FrameNet, the resource to which it is most frequently compared, in several ways.. PropBank is a verb-oriented resource, while FrameNet is centered on the more abstract notion of frames, which generalizes descriptions across similar verbs (e.g. "describe" and "characterize") as well as nouns and other words (e.g. "description"). [2]
Finally, with the frame-semantic paradigm's analytical tools, the linguist is able to explain a wider range of semantic phenomena than they would be able to with only necessary and sufficient conditions. Some words have the same definitions or intensions, and the same extensions, but have subtly different domains.