A semantic decomposition is an algorithm that breaks down the meanings of phrases or concepts into less complex concepts.
The result of a semantic decomposition is a representation of meaning. This representation can be used for tasks, such as those related to
artificial intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech re ...
or
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
. Semantic decomposition is common in
natural language processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to pro ...
applications.
The basic idea of a semantic decomposition is taken from the learning skills of adult humans, where words are explained using other words. It is based on
Meaning-text theory. Meaning-text theory is used as a theoretical linguistic framework to describe the meaning of concepts with other concepts.
Background
Given that an
AI does not inherently have language, it is unable to think about the meanings behind the words of a language. An artificial notion of meaning needs to be created for a strong AI to emerge. AI today is able to capture the syntax of language for many specific problems, but never establishes meaning for the words of these languages, nor is it able to abstract these words to higher-order concepts
Creating an artificial representation of meaning requires the analysis of what meaning is. Many terms are associated with meaning, including semantics, pragmatics, knowledge and understanding or word sense. Each term describes a particular aspect of meaning, and contributes to a multitude of theories explaining what meaning is. These theories need to be analyzed further to develop an artificial notion of meaning best fit for our current state of knowledge.
Graph representations
Representing meaning as a graph is one of the two ways that both an AI cognition and a linguistic researcher think about meaning (
connectionist
Connectionism refers to both an approach in the field of cognitive science that hopes to explain mental phenomena using artificial neural networks (ANN) and to a wide range of techniques and algorithms using ANNs in the context of artificial int ...
view). Logicians utilize a formal representation of meaning to build upon the idea of symbolic representation, whereas description logics describe languages and the meaning of symbols. This contention between 'neat' and 'scruffy' techniques has been discussed since the 1970s.
Research has so far identified semantic measures and with that
word-sense disambiguation
Word-sense disambiguation (WSD) 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/automatic but can often come to consc ...
(WSD) - the differentiation of meaning of words - as the main problem of language understanding. As an AI-complete environment, WSD is a core problem of natural language understanding. AI approaches that use knowledge-given reasoning creates a notion of meaning combining the state of the art knowledge of natural meaning with the symbolic and connectionist formalization of meaning for AI. The abstract approach is shown in Figure. First, a connectionist knowledge representation is created as a
semantic network
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, ...
consisting of concepts and their relations to serve as the basis for the representation of meaning.
This graph is built out of different knowledge sources like
WordNet
WordNet is a lexical database of semantic relations between words in more than 200 languages. WordNet links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into '' synsets'' with short definition ...
,
Wiktionary
Wiktionary ( , , rhyming with "dictionary") is a multilingual, web-based project to create a free content dictionary of terms (including words, phrases, proverbs, linguistic reconstructions, etc.) in all natural languages and in a number ...
, and BabelNET. The graph is created by lexical decomposition that
recursively
Recursion (adjective: ''recursive'') occurs when a thing is defined in terms of itself or of its type. Recursion is used in a variety of disciplines ranging from linguistics to logic. The most common application of recursion is in mathematics ...
breaks each concept semantically down into a set of
semantic primes
Semantic primes or semantic primitives are a set of semantic concepts that are argued to be innately understood by all people but impossible to express in simpler terms. They represent words or phrases that are learned through practice but cannot ...
.
The primes are taken from the theory of
Natural Semantic Metalanguage
The natural semantic metalanguage (NSM) is a linguistic theory that reduces lexicons down to a set of semantic primitives. It is based on the conception of Polish professor Andrzej Bogusławski. The theory was formally developed by Anna Wierzbi ...
, which has been analyzed for usefulness in formal languages. Upon this graph marker passing is used to create the dynamic part of meaning representing thoughts.
The marker passing algorithm, where symbolic information is passed along relations form one concept to another, uses node and edge interpretation to guide its markers. The node and edge interpretation model is the symbolic influence of certain concepts.
Future work uses the created representation of meaning to build heuristics and evaluate them through capability matching and agent planning,
chatbot
A chatbot or chatterbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. Designed to convincingly simulate the way a human would behav ...
s or other applications of
natural language understanding
Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an ...
.
See also
*
Latent Semantic Analysis
Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the do ...
*
Lexical semantics
Lexical semantics (also known as lexicosemantics), as a subfield of linguistic semantics, is the study of word meanings.Pustejovsky, J. (2005) Lexical Semantics: Overview' in Encyclopedia of Language and Linguistics, second edition, Volumes 1-14Ta ...
*
Principle of compositionality
In semantics, mathematical logic and related disciplines, the principle of compositionality is the principle that the meaning of a complex expression is determined by the meanings of its constituent expressions and the rules used to combine them. ...
References
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Natural language processing
Semantics