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The connection of generalization to specialization (or particularization) is reflected in the contrasting words hypernym and hyponym.A hypernym as a generic stands for a class or group of equally ranked items, such as the term tree which stands for equally ranked items such as peach and oak, and the term ship which stands for equally ranked items such as cruiser and steamer.
WordNet is a lexical database of semantic relations between words that links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into synsets with short definitions and usage examples. It can thus be seen as a combination and extension of a dictionary and thesaurus.
On the other hand, formal equivalence can allow readers familiar with the source language to analyze how meaning was expressed in the original text, preserving untranslated idioms, rhetorical devices (such as chiastic structures in the Hebrew Bible) and diction in order to preserve original information and highlight finer shades of meaning.
Relate unstructured text with structured data such as dates, numbers or categorical data for identifying temporal trends or differences between subgroups or for assessing relationship with ratings or other kind of categorical or numerical data. Visualization tools to visualize and interpret text analysis results: Dendrogram with optional bar chart
When helping a child learn a new word, providing more examples of the word increases the child's capacity to generalize the word to different contexts and situations. Furthermore, writing interventions for grade-school students yield better results when the intervention actively targets generalization as an outcome.
Voyant Tools is an open-source, web-based application for performing text analysis. It supports scholarly reading and interpretation of texts or corpus, particularly by scholars in the digital humanities, but also by students and the general public. It can be used to analyze online texts or ones uploaded by users. [1]
Quantitative textual analysis models often employ 'bag of words' methods that remove word ordering, delete words that are very common and very uncommon, and simplify words through lemmatisation or stemming that reduces the dimensionality of the text by reducing complex words to their root word. [10]
The BoW representation of a text removes all word ordering. For example, the BoW representation of "man bites dog" and "dog bites man" are the same, so any algorithm that operates with a BoW representation of text must treat them in the same way. Despite this lack of syntax or grammar, BoW representation is fast and may be sufficient for simple ...