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Co-occurrence
In linguistics, co-occurrence or cooccurrence is an above-chance frequency of occurrence of two terms (also known as coincidence or concurrence) from a text corpus alongside each other in a certain order. 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 correlation or mutual information. See also * Distributional hypothesis * Statistical semantics * Co-occurrence matrix * Co-occurrence networks * Similarity ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Co-occurrence Networks
Co-occurrence network, sometimes referred to as a semantic network, is a method to analyze text that includes a graphic visualization of potential relationships between people, organizations, concepts, biological organisms like bacteria or other entities represented within written material. The generation and visualization of co-occurrence networks has become practical with the advent of electronically stored text compliant to text mining. By way of definition, co-occurrence networks are the collective interconnection of terms based on their paired presence within a specified unit of text. Networks are generated by connecting pairs of terms using a set of criteria defining co-occurrence. For example, terms A and B may be said to “co-occur” if they both appear in a particular article. Another article may contain terms B and C. Linking A to B and B to C creates a co-occurrence network of these three terms. Rules to define co-occurrence within a text corpus can be set according ... [...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 that is defined over an 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 occur in the relation given by the offse ... [...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 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 in the fi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Distributional Hypothesis
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.'' Distributional hypothesis The distributional hypothesis in linguistics is derived from the semantic theory of language usage, i.e. words that are used and occur in the same contexts tend to purport similar meanings. The underlying idea that "a word is characterized by the company it keeps" was popularized by Firth in the 1950s. The distributional hypothesis is the basis for statistical semantics. Although the Distributional Hypothesis originated in linguistics, it is now receiving attention in cognitive science especially regarding the context of word use. In rec ... [...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] |
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 so-called 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 ... [...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 in clustering In spectral clustering, a similarity, or affinity, measure is used to transform data to overcome difficulties related to lack of convexity in the shape of the data distribut ... [...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 dependence between the two variables. More specifically, it quantifies the " amount of information" (in units such as shannons (bits), nats or hartleys) obtained about one random variable by observing the other random variable. The concept of mutual information is intimately linked to that of 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 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 Y. MI is the expected value of the pointwise mutual information (PMI). The quantity was defined and analyzed by Claude Shannon in his landmark paper "A Mathemati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Word Count
The word count is the number of words in a document or passage of text. Word counting may be needed when a text is required to stay within certain numbers of words. This may particularly be the case in academia, legal proceedings, journalism and advertising. Word count is commonly used by translators to determine the price of a translation job. Word counts may also be used to calculate measures of readability and to measure typing and reading speeds (usually in words per minute). When converting character counts to words, a measure of 5 or 6 characters to a word is generally used for English. Details and variations of definition Variations in the operational definitions of how to count the words can occur (namely, what "counts as" a word, and which words "don't count" toward the total). However, especially since the advent of widespread word processing, there is a broad consensus on these operational definitions (and hence the bottom-line integer result). The consensus is to acc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Word
A word is a basic element of language that carries an semantics, objective or pragmatics, practical 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Language Structure
In linguistics, the grammar of a natural language is its set of structural constraints on speakers' or writers' composition of clauses, phrases, and words. The term can also refer to the study of such constraints, a field that includes domains such as phonology, morphology, and syntax, often complemented by phonetics, semantics, and pragmatics. There are currently two different approaches to the study of grammar: traditional grammar and theoretical grammar. Fluent speakers of a language variety or ''lect'' have effectively internalized these constraints, the vast majority of which – at least in the case of one's native language(s) – are acquired not by conscious study or instruction but by hearing other speakers. Much of this internalization occurs during early childhood; learning a language later in life usually involves more explicit instruction. In this view, grammar is understood as the cognitive information underlying a specific instance of language production. ... [...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. An example of a phraseological collocation is the expression ''strong tea''. While the same meaning could be conveyed by the roughly equivalent ''powerful tea'', this adjective does not modify ''tea'' frequently enough for English speakers to become accustomed to its co-occurrence and regard it as idiomatic or unmarked. (By way of counterexample, ''powerful'' is idiomatically preferred to ''strong'' when modifying a ''computer'' or a ''car''.) There are about six main types of collocations: adjective + noun, noun + noun (such as collective nouns), verb + noun, adverb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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