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The syntax is keyword1 near:n keyword2 where n=the number of maximum separating words. Ordered search within the Google and Yahoo! search engines is possible using the asterisk (*) full-word wildcards: in Google this matches one or more words, [9] and an in Yahoo! Search this matches exactly one word. [10] (This is easily verified by searching ...
A comma separated list of the fields to use. Allowed fields are title, text, auxiliary_text, opening_text, headings and all. &cirrusMltUseFields (true or false) use only the field data. Defaults to false: the system will extract the content of the text field to build the query. &cirrusMltPercentTermsToMatch: The percentage of terms to match on.
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus.
for every sense of the word being disambiguated one should count the number of words that are in both the neighborhood of that word and in the dictionary definition of that sense; the sense that is to be chosen is the sense that has the largest number of this count. A frequently used example illustrating this algorithm is for the context "pine ...
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
The bag-of-words model (BoW) is a model of text which uses a representation of text that is based on an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity.
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
In linguistic morphology and information retrieval, stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form—generally a written word form. The stem need not be identical to the morphological root of the word; it is usually sufficient that related words map to the same stem, even if this ...