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Co-occurrence
In linguistics, co-occurrence or cooccurrence is an above-chance frequency of ordered occurrence of two adjacent terms in a text corpus. Co-occurrence in this linguistic sense can be interpreted as an indicator of semantic proximity or an idiomatic expression. Corpus linguistics and its statistic analyses reveal patterns of co-occurrences within a language and enable to work out typical collocations for its lexical items. A ''co-occurrence restriction'' is identified when linguistic elements never occur together. Analysis of these restrictions can lead to discoveries about the structure and development of a language. Co-occurrence can be seen an extension of word counting in higher dimensions. Co-occurrence can be quantitatively described using measures like a massive correlation or mutual information. See also * Distributional hypothesis * Statistical semantics * Idiom (language structure) * Co-occurrence matrix * Co-occurrence networks * Similarity measure * Dice ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Co-occurrence Matrix
A co-occurrence matrix or co-occurrence distribution (also referred to as : ''gray-level co-occurrence matrices'' GLCMs) is a matrix (mathematics), matrix that is defined over an Digital image, image to be the distribution of co-occurring pixel values (grayscale values, or colors) at a given offset. It is used as an approach to texture analysis with various applications especially in medical image analysis. Method Given a grey-level image I, co-occurrence matrix computes how often pairs of pixels with a specific value and offset occur in the image. * The offset, (\Delta x, \Delta y), is a position operator that can be applied to any pixel in the image (ignoring edge effects): for instance, (1, 2) could indicate "one down, two right". * An image with p different pixel values will produce a p \times p co-occurrence matrix, for the given offset. * The (i, j)^\text value of the co-occurrence matrix gives the number of times in the image that the i^\text and j^\text pixel values occ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Co-occurrence Networks
In linguistics, co-occurrence or cooccurrence is an above-chance frequency of ordered occurrence of two adjacent terms in a text corpus. Co-occurrence in this linguistic sense can be interpreted as an indicator of semantic proximity or an idiomatic expression. Corpus linguistics and its statistic analyses reveal patterns of co-occurrences within a language and enable to work out typical collocations for its lexical items. A ''co-occurrence restriction'' is identified when linguistic elements never occur together. Analysis of these restrictions can lead to discoveries about the structure and development of a language. Co-occurrence can be seen an extension of word counting in higher dimensions. Co-occurrence can be quantitatively described using measures like a massive correlation or mutual information. See also * Distributional hypothesis * Statistical semantics * Idiom (language structure) * Co-occurrence matrix * Co-occurrence networks * Similarity measure In statistics ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Collocation
In corpus linguistics, a collocation is a series of words or terms that co-occur more often than would be expected by chance. In phraseology, a collocation is a type of compositional phraseme, meaning that it can be understood from the words that make it up. This contrasts with an idiom, where the meaning of the whole cannot be inferred from its parts, and may be completely unrelated. There are about seven main types of collocations: adjective + noun, noun + noun (such as collective nouns), noun + verb, verb + noun, adverb + adjective, verbs + prepositional phrase ( phrasal verbs), and verb + adverb. Collocation extraction is a computational technique that finds collocations in a document or corpus, using various computational linguistics elements resembling data mining. Expanded definition Collocations are partly or fully fixed expressions that become established through repeated context-dependent use. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Semantics
In linguistics, statistical semantics applies the methods of statistics to the problem of determining the meaning of words or phrases, ideally through unsupervised learning, to a degree of precision at least sufficient for the purpose of information retrieval. History The term ''statistical semantics'' was first used by Warren Weaver in his well-known paper on machine translation. He argued that word-sense disambiguation for machine translation should be based on the co-occurrence frequency of the context words near a given target word. The underlying assumption that "a word is characterized by the company it keeps" was advocated by J. R. Firth. This assumption is known in linguistics as the distributional hypothesis. Emile Delavenay defined ''statistical semantics'' as the "statistical study of the meanings of words and their frequency and order of recurrence". " Furnas et al. 1983" is frequently cited as a foundational contribution to statistical semantics. An early success i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mutual Information
In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual Statistical dependence, dependence between the two variables. More specifically, it quantifies the "Information content, amount of information" (in Units of information, units such as shannon (unit), shannons (bits), Nat (unit), nats or Hartley (unit), hartleys) obtained about one random variable by observing the other random variable. The concept of mutual information is intimately linked to that of Entropy (information theory), entropy of a random variable, a fundamental notion in information theory that quantifies the expected "amount of information" held in a random variable. Not limited to real-valued random variables and linear dependence like the Pearson correlation coefficient, correlation coefficient, MI is more general and determines how different the joint distribution of the pair (X,Y) is from the product of the marginal distributions of X and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semantic Proximity
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". Computationally, semantic similarity can be estimated by defining a topological similarity, by using ontologies to define the distance between terms/concepts. For example, a naive metric for the comparison of concepts orde ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Occurrence (type–token Distinction)
Occurrence may refer to: * Occurrence (type–token distinction), concept in type–token distinction * Occurrence (liturgical), Catholic liturgical term that covers the process when two liturgical offices coincide on the same day {{disambiguation ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Similarity Measure
In statistics and related fields, a similarity measure or similarity function or similarity metric is a real-valued function that quantifies the similarity between two objects. Although no single definition of a similarity exists, usually such measures are in some sense the inverse of distance metrics: they take on large values for similar objects and either zero or a negative value for very dissimilar objects. Though, in more broad terms, a similarity function may also satisfy metric axioms. Cosine similarity is a commonly used similarity measure for real-valued vectors, used in (among other fields) information retrieval to score the similarity of documents in the vector space model. In machine learning, common kernel functions such as the RBF kernel can be viewed as similarity functions. Use of different similarity measure formulas Different types of similarity measures exist for various types of objects, depending on the objects being compared. For each type of object there ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Idiom (language Structure)
An idiom (the quality of it being known as idiomaticness or idiomaticity) is a syntactical, grammatical, or phonological structure peculiar to a language that is actually realized, as opposed to possible but unrealized structures that could have developed to serve the same semantic functions but did not. The grammar of a language (its morphology, phonology, and syntax) is inherently arbitrary and peculiar to a specific language (or group of related languages). For example, although in English it is idiomatic (accepted as structurally correct) to say "cats are associated with agility", other forms could have developed, such as "cats associate toward agility" or "cats are associated of agility". Unidiomatic constructions sound wrong to fluent speakers, although they are often entirely comprehensible. For example, the title of the classic book '' English as She Is Spoke'' is easy to understand (its idiomatic counterpart is ''English as It Is Spoken''), but it deviates from Engli ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Correlation
In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are '' linearly'' related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the demand curve. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However, in g ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Word
A word is a basic element of language that carries semantics, meaning, can be used on its own, and is uninterruptible. Despite the fact that language speakers often have an intuitive grasp of what a word is, there is no consensus among linguistics, linguists on its definition and numerous attempts to find specific criteria of the concept remain controversial. Different standards have been proposed, depending on the theoretical background and descriptive context; these do not converge on a single definition. Some specific definitions of the term "word" are employed to convey its different meanings at different levels of description, for example based on phonology, phonological, grammar, grammatical or orthography, orthographic basis. Others suggest that the concept is simply a convention used in everyday situations. The concept of "word" is distinguished from that of a morpheme, which is the smallest unit of language that has a meaning, even if it cannot stand on its own. Words a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
