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Keyword (linguistics)
In corpus linguistics a key word is a word which occurs in a text more often than we would expect to occur by chance alone.Scott, M. & Tribble, C., 2006, ''Textual Patterns: keyword and corpus analysis in language education'', Amsterdam: Benjamins, 55. Key words are calculated by carrying out a statistical test (e.g., loglinear or chi-squared) which compares the word frequencies in a text against their expected frequencies derived in a much larger corpus, which acts as a reference for general language use. Keyness is then the quality a word or phrase has of being "key" in its context. Compare this with 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 th ..., the quality linking two words or phrases usually assumed to be within a given span of each other. Keyness is a ''textu ...
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Corpus Linguistics
Corpus linguistics is the study of language, study of a language as that language is expressed in its text corpus (plural ''corpora''), its body of "real world" text. Corpus linguistics proposes that a reliable analysis of a language is more feasible with corpora collected in the field—the natural context ("realia") of that language—with minimal experimental interference. The text-corpus method uses the body of texts written in any natural language to derive the set of abstract rules which govern that language. Those results can be used to explore the relationships between that subject language and other languages which have undergone a similar analysis. The first such corpora were manually derived from source texts, but now that work is automated. Corpora have not only been used for linguistics research, they have also been used to compile dictionaries (starting with ''The American Heritage Dictionary of the English Language'' in 1969) and grammar guides, such as ''A Compreh ...
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Statistical Test
A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. History Early use While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see . Modern origins and early controversy Modern significance testing is largely the product of Karl Pearson ( ''p''-value, Pearson's chi-squared test), William Sealy Gosset (Student's t-distribution), and Ronald Fisher ("null hypothesis", analysis of variance, "significance test"), while hypothesis testing was developed by Jerzy Neyman and Egon Pearson (son of Karl). Ronald Fisher began his life in statistics as a Bayesian (Zabell 1992), but Fisher soon grew disenchanted with the s ...
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Log-linear Analysis
Log-linear analysis is a technique used in statistics to examine the relationship between more than two categorical variables. The technique is used for both hypothesis testing and model building. In both these uses, models are tested to find the most parsimonious (i.e., least complex) model that best accounts for the variance in the observed frequencies. (A Pearson's chi-square test could be used instead of log-linear analysis, but that technique only allows for two of the variables to be compared at a time.) Fitting criterion Log-linear analysis uses a likelihood ratio statistic \Chi^2 that has an approximate chi-square distribution when the sample size is large: :\Chi^2 = 2\sum O_ \ln \frac, where :\ln = natural logarithm; :O_ = observed frequency in cell''ij'' (''i'' = row and ''j'' = column); :E_ = expected frequency in cell''ij''. :\Chi^2 = the deviance for the model. Assumptions There are three assumptions in log-linear analysis: 1. The observations are independent a ...
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Chi-squared Test
A chi-squared test (also chi-square or test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are large. In simpler terms, this test is primarily used to examine whether two categorical variables (''two dimensions of the contingency table'') are independent in influencing the test statistic (''values within the table''). The test is valid when the test statistic is chi-squared distributed under the null hypothesis, specifically Pearson's chi-squared test and variants thereof. Pearson's chi-squared test is used to determine whether there is a statistically significant difference between the expected frequencies and the observed frequencies in one or more categories of a contingency table. For contingency tables with smaller sample sizes, a Fisher's exact test is used instead. In the standard applications of this test, the observations are classified into mutually exclusive classes. If the null hypothesis that there are no di ...
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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 ...
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