Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning.
Natural language processing (NLP) is an interdisciplinary subfield of computer science and artificial intelligence. It is primarily concerned with providing computers the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics. Typically data is collected in text corpora, using either rule-based, statistical or neural-based approaches of machine learning and deep learning.
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, mapping or connecting semantic fields. 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.
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.
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 Wierzbicka at Warsaw University and later at the Australian National University in the early 1970s, and Cliff Goddard at Australia's Griffith University.
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.
The language of thought hypothesis (LOTH), sometimes known as thought ordered mental expression (TOME), is a view in linguistics, philosophy of mind and cognitive science, forwarded by American philosopher Jerry Fodor. It describes the nature of thought as possessing "language-like" or compositional structure. On this view, simple concepts combine in systematic ways to build thoughts. In its most basic form, the theory states that thought, like language, has syntax.
Conceptual semantics is a framework for semantic analysis developed mainly by Ray Jackendoff in 1976. Its aim is to provide a characterization of the conceptual elements by which a person understands words and sentences, and thus to provide an explanatory semantic representation. Explanatory in this sense refers to the ability of a given linguistic theory to describe how a component of language is acquired by a child.
Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of language, concepts or instances, through a numerical description obtained according to the comparison of information supporting their meaning or describing their nature. The term semantic similarity is often confused with semantic relatedness. Semantic relatedness includes any relation between two terms, while semantic similarity only includes "is a" relations. For example, "car" is similar to "bus", but is also related to "road" and "driving".
Cognitive semantics is part of the cognitive linguistics movement. Semantics is the study of linguistic meaning. Cognitive semantics holds that language is part of a more general human cognitive ability, and can therefore only describe the world as people conceive of it. It is implicit that different linguistic communities conceive of simple things and processes in the world differently, not necessarily some difference between a person's conceptual world and the real world.
Frame semantics is a theory of linguistic meaning developed by Charles J. Fillmore that extends his earlier case grammar. It relates linguistic semantics to encyclopedic knowledge. The basic idea is that one cannot understand the meaning of a single word without access to all the essential knowledge that relates to that word. For example, one would not be able to understand the word "sell" without knowing anything about the situation of commercial transfer, which also involves, among other things, a seller, a buyer, goods, money, the relation between the money and the goods, the relations between the seller and the goods and the money, the relation between the buyer and the goods and the money and so on. Thus, a word activates, or evokes, a frame of semantic knowledge relating to the specific concept to which it refers.
Distributional semantics is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in large samples of language data. The basic idea of distributional semantics can be summed up in the so-called distributional hypothesis: linguistic items with similar distributions have similar meanings.
Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes. Most often these "weights" are real values that decay as activation propagates through the network. When the weights are discrete this process is often referred to as marker passing. Activation may originate from alternate paths, identified by distinct markers, and terminate when two alternate paths reach the same node. However brain studies show that several different brain areas play an important role in semantic processing.
DOGMA, short for Developing Ontology-Grounded Methods and Applications, is the name of research project in progress at Vrije Universiteit Brussel's STARLab, Semantics Technology and Applications Research Laboratory. It is an internally funded project, concerned with the more general aspects of extracting, storing, representing and browsing information.
The linguistics wars were extended disputes among American theoretical linguists that occurred mostly during the 1960s and 1970s, stemming from a disagreement between Noam Chomsky and several of his associates and students. The debates started in 1967 when linguists Paul Postal, John R. Ross, George Lakoff, and James D. McCawley —self-dubbed the "Four Horsemen of the Apocalypse"—proposed an alternative approach in which the relation between semantics and syntax is viewed differently, which treated deep structures as meaning rather than syntactic objects. While Chomsky and other generative grammarians argued that meaning is driven by an underlying syntax, generative semanticists posited that syntax is shaped by an underlying meaning. This intellectual divergence led to two competing frameworks in generative semantics and interpretive semantics.
Meaning–text theory (MTT) is a theoretical linguistic framework, first put forward in Moscow by Aleksandr Žolkovskij and Igor Mel’čuk, for the construction of models of natural language. The theory provides a large and elaborate basis for linguistic description and, due to its formal character, lends itself particularly well to computer applications, including machine translation, phraseology, and lexicography.
In computational linguistics, word-sense induction (WSI) or discrimination is an open problem of natural language processing, which concerns the automatic identification of the senses of a word. Given that the output of word-sense induction is a set of senses for the target word, this task is strictly related to that of word-sense disambiguation (WSD), which relies on a predefined sense inventory and aims to solve the ambiguity of words in context.
SemEval is an ongoing series of evaluations of computational semantic analysis systems; it evolved from the Senseval word sense evaluation series. The evaluations are intended to explore the nature of meaning in language. While meaning is intuitive to humans, transferring those intuitions to computational analysis has proved elusive.
The following outline is provided as an overview of and topical guide to natural-language processing:
In natural language processing (NLP), 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. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.
