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Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document. [1] [2] Key phrases, key terms, key segments or just keywords are the terminology which is used for defining the terms that represent the most relevant information contained in the document. Although the terminology is different ...
The objective of keyword research is to generate, with good precision and recall, a large number of terms that are highly relevant yet non-obvious to the given input keyword. [1] The process of keyword research involves brainstorming and the use of keyword research tools, with popular ones including Semrush and Google Trends.
After pre-processing the text data, we can then proceed to generate features. For document clustering, one of the most common ways to generate features for a document is to calculate the term frequencies of all its tokens. Although not perfect, these frequencies can usually provide some clues about the topic of the document.
The task is the following. You are given a piece of text, such as a journal article, and you must produce a list of keywords or key[phrase]s that capture the primary topics discussed in the text. [14] In the case of research articles, many authors provide manually assigned keywords, but most text lacks pre-existing keyphrases. For example, news ...
It was a useful indexing method for technical manuals before computerized full text search became common. For example, a search query including all of the words in an example definition ("KWIC is an acronym for Key Word In Context, the most common format for concordance lines") and the Wikipedia slogan in English ("the free encyclopedia ...
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. [1] 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.
Automatic taxonomy construction (ATC) is the use of software programs to generate taxonomical classifications from a body of texts called a corpus.ATC is a branch of natural language processing, which in turn is a branch of artificial intelligence.
IWE combines Word2vec with a semantic dictionary mapping technique to tackle the major challenges of information extraction from clinical texts, which include ambiguity of free text narrative style, lexical variations, use of ungrammatical and telegraphic phases, arbitrary ordering of words, and frequent appearance of abbreviations and acronyms ...