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The basic linguistic assumption of proximity searching is that the proximity of the words in a document implies a relationship between the words. Given that authors of documents try to formulate sentences which contain a single idea, or cluster of related ideas within neighboring sentences or organized into paragraphs, there is an inherent, relatively high, probability within the document ...
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.
Whatever you type into the search box is called the "search string". It may also be referred to as the "search query". A basic search string is simply the topic you are interested in reading about. A direct match of a basic search string will navigate you directly to Wikipedia's article that has that title. A no
Multi-document summarization may also be done in response to a question. [20] [11] Multi-document summarization creates information reports that are both concise and comprehensive. With different opinions being put together and outlined, every topic is described from multiple perspectives within a single document.
In a larger search engine, the process of finding each word in the inverted index (in order to report that it occurred within a document) may be too time consuming, and so this process is commonly split up into two parts, the development of a forward index and a process which sorts the contents of the forward index into the inverted index.
Algebraic models represent documents and queries usually as vectors, matrices, or tuples. The similarity of the query vector and document vector is represented as a scalar value. Vector space model; Generalized vector space model (Enhanced) Topic-based Vector Space Model; Extended Boolean model; Latent semantic indexing a.k.a. latent semantic ...
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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. And sometimes it is also useful to weight the term frequencies by the inverse document frequencies.