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Pointwise Mutual Information
In statistics, probability theory and information theory, pointwise mutual information (PMI), or point mutual information, is a measure of Association (statistics), association. It compares the probability of two events occurring together to what this probability would be if the events were Independence (probability theory), independent.Dan Jurafsky and James H. Martin: Speech and Language Processing (3rd ed. draft), December 29, 2021chapter 6 PMI (especially in its positive pointwise mutual information variant) has been described as "one of the most important concepts in Natural language processing, NLP", where it "draws on the intuition that the best way to weigh the association between two words is to ask how much more the two words co-occur in [a] corpus than we would have expected them to appear by chance." The concept was introduced in 1961 by Robert Fano under the name of "mutual information", but today that term is instead used for a related measure of dependence between r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistics
Statistics (from German language, German: ', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of statistical survey, surveys and experimental design, experiments. When census data (comprising every member of the target population) cannot be collected, statisticians collect data by developing specific experiment designs and survey sample (statistics), samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Marginalization (probability)
In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the variables contained in the subset. It gives the probabilities of various values of the variables in the subset without reference to the values of the other variables. This contrasts with a conditional distribution, which gives the probabilities contingent upon the values of the other variables. Marginal variables are those variables in the subset of variables being retained. These concepts are "marginal" because they can be found by summing values in a table along rows or columns, and writing the sum in the margins of the table. The distribution of the marginal variables (the marginal distribution) is obtained by marginalizing (that is, focusing on the sums in the margin) over the distribution of the variables being discarded, and the discarded variables are said to have been marginalized out. The context here is that the theoretica ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Theory
Information theory is the mathematical study of the quantification (science), quantification, Data storage, storage, and telecommunications, communication of information. The field was established and formalized by Claude Shannon in the 1940s, though early contributions were made in the 1920s through the works of Harry Nyquist and Ralph Hartley. It is at the intersection of electronic engineering, mathematics, statistics, computer science, Neuroscience, neurobiology, physics, and electrical engineering. A key measure in information theory is information entropy, entropy. Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. For example, identifying the outcome of a Fair coin, fair coin flip (which has two equally likely outcomes) provides less information (lower entropy, less uncertainty) than identifying the outcome from a roll of a dice, die (which has six equally likely outcomes). Some other important measu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Text Corpus
In linguistics and natural language processing, a corpus (: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated. Annotated, they have been used in corpus linguistics for statistical statistical hypothesis testing, hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory. Overview A corpus may contain texts in a single language (''monolingual corpus'') or text data in multiple languages (''multilingual corpus''). In order to make the corpora more useful for doing linguistic research, they are often subjected to a process known as annotation. An example of annotating a corpus is part-of-speech tagging, or ''POS-tagging'', in which information about each word's part of speech (verb, noun, adjective, etc.) is added to the corpus in the form of ''tags''. Another example is indicating the Lemma (morphology), lemma (base) form of each word ... [...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. Software Modern web browsers support word counting via extensions, via a JavaScript bookmarklet, or a script that is hosted in a website. Most word processors can also count words. Unix-like systems include a program, '' wc'', specifically for word counting. There are a wide variety of word counting tools available online. Different word counting programs may give varying results, depend ... [...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] |
Computational Linguistics
Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others. Computational linguistics is closely related to mathematical linguistics. Origins The field overlapped with artificial intelligence since the efforts in the United States in the 1950s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English. Since rule-based approaches were able to make arithmetic (systematic) calculations much faster and more accurately than humans, it was expected that lexicon, morphology, syntax and semantics can be learned using explicit rules, a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Chain Rule (probability)
In probability theory, the chain rule (also called the general product rule) describes how to calculate the probability of the intersection of, not necessarily independent, events or the joint distribution of random variables respectively, using conditional probabilities. This rule allows one to express a joint probability in terms of only conditional probabilities. The rule is notably used in the context of discrete stochastic processes and in applications, e.g. the study of Bayesian networks, which describe a probability distribution in terms of conditional probabilities. Chain rule for events Two events For two events A and B, the chain rule states that :\mathbb P(A \cap B) = \mathbb P(B \mid A) \mathbb P(A), where \mathbb P(B \mid A) denotes the conditional probability of B given A. Example An Urn A has 1 black ball and 2 white balls and another Urn B has 1 black ball and 3 white balls. Suppose we pick an urn at random and then select a ball from that urn. Let event A be c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Total Correlation
In probability theory and in particular in information theory, total correlation (Watanabe 1960) is one of several generalizations of the mutual information. It is also known as the ''multivariate constraint'' (Garner 1962) or ''multiinformation'' (Studený & Vejnarová 1999). It quantifies the redundancy or dependency among a set of ''n'' random variables. Definition For a given set of ''n'' random variables \, the total correlation C(X_1,X_2,\ldots,X_n) is defined as the Kullback–Leibler divergence from the joint distribution p(X_1, \ldots, X_n) to the independent distribution of p(X_1)p(X_2)\cdots p(X_n), :C(X_1, X_2, \ldots, X_n) \equiv \operatorname\left p(X_1)p(X_2)\cdots p(X_n)\right\; . This divergence reduces to the simpler difference of entropies, :C(X_1,X_2,\ldots,X_n) = \left sum_^n H(X_i)\right- H(X_1, X_2, \ldots, X_n) where H(X_) is the information entropy of variable X_i \,, and H(X_1,X_2,\ldots,X_n) is the joint entropy of the variable set \. In terms of the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Béatrice Daille (linguist)
Béatrice is a French feminine given name. Notable people with the name include: * Béatrice Bonifassi (born ), French-born vocalist * Béatrice Dalle (born 1964), French actress * Béatrice de Camondo (1894–1944), French socialite and a Holocaust victim * Béatrice de Planisoles, minor noble in the Comté de Foix in the late thirteenth and early fourteenth century * Béatrice Descamps (born 1951), French politician and a member of the Senate of France * Béatrice Ephrussi de Rothschild (1864–1934), French socialite * Béatrice Farinacci, former French figure skater * Béatrice Gosselin (born 1958), French politician * Béatrice Hess (born 1961 or 1962), French swimmer * Béatrice Hiéronyme de Lorraine (1662–1738), member of the House of Lorraine * Béatrice Knopf-Basson (born 1958), French sprint canoer * Béatrice Lalinon Gbado, children's writer * Béatrice Longuenesse, professor of philosophy at New York University * Béatrice Martin, (born 1989), French-Canadian ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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] |
Self-information
In information theory, the information content, self-information, surprisal, or Shannon information is a basic quantity derived from the probability of a particular event occurring from a random variable. It can be thought of as an alternative way of expressing probability, much like odds or log-odds, but which has particular mathematical advantages in the setting of information theory. The Shannon information can be interpreted as quantifying the level of "surprise" of a particular outcome. As it is such a basic quantity, it also appears in several other settings, such as the length of a message needed to transmit the event given an optimal source coding of the random variable. The Shannon information is closely related to ''entropy'', which is the expected value of the self-information of a random variable, quantifying how surprising the random variable is "on average". This is the average amount of self-information an observer would expect to gain about a random variable wh ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
