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Automatic summarization – process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. Often used to provide summaries of text of a known type, such as articles in the financial section of a newspaper. Types Keyphrase extraction –
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
A set of NLP sentences, with associated ontology defined, can also be used as a pseudo code that does not provide the details in any underlying high level programming language. In such an application the sentences used become high level abstractions (conceptualisations) of computing procedures that are computer language and machine independent.
TextRank is a general purpose graph-based ranking algorithm for NLP. Essentially, it runs PageRank on a graph specially designed for a particular NLP task. For keyphrase extraction, it builds a graph using some set of text units as vertices. Edges are based on some measure of semantic or lexical similarity between the text unit vertices. Unlike ...
Text corpora are also used in the study of historical documents, for example in attempts to decipher ancient scripts, or in Biblical scholarship. Some archaeological corpora can be of such short duration that they provide a snapshot in time. One of the shortest corpora in time may be the 15–30 year Amarna letters texts .
Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.
In natural language processing a w-shingling is a set of unique shingles (therefore n-grams) each of which is composed of contiguous subsequences of tokens within a document, which can then be used to ascertain the similarity between documents. The symbol w denotes the quantity of tokens in each shingle selected, or solved for.
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 ...