Method and system for automatically extracting keywords from multiple documents and computer program

Abstract

The invention belongs to the technical field of computer software, and discloses a method and system for automatically extracting keywords from multiple documents and a computer program. The method comprises the steps of extracting keyword seeds; utilizing statistical information of the mutual positions of words in text to measure whether or not a combined word is semantically integrated; if yes,determining the combined word to be a key combined word; if not, conducting left and right expansion. By means of the extraction method of discovering semantic integrity and cutting off reasonable keyphrases, the readability of a result obtained by automatically extracting the keywords from multiple documents is greatly improved; the extracted keywords have a larger average length, and the themesof multiple documents can be better described through more abundant and integrated semantics; the keywords extracted by a Tf-Idf algorithm are more detailed, the semantics is broader and not specific, and the themes of the documents cannot be represented; the practicality is ensured, labor cost for annotation is saved, and the automatic keyword extraction can be conducted without any annotated corpora.

Description

technical field [0001] The invention belongs to the technical field of computer software, and in particular relates to a multi-document keyword automatic extraction method and system, and a computer program. Background technique [0002] A word is the smallest unit for expressing semantics, and a keyword is a collection of words or phrases that best represent the theme of a certain piece of text. Keyword automatic extraction technology is an automatic technology for identifying meaningful and representative segments or words. Automatic keyword extraction helps readers quickly and accurately grasp the topic of the article; and this technology has good applications in various scenarios such as automatic summarization, information retrieval, and information extraction. At present, keyword extraction methods are divided into two categories according to whether they are supervised or not: 1) Unsupervised algorithm, which does not require heavy labeling work, and can automaticall...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the current Tf-Idf algorithm is poor in identifying the boundaries of compound words, and compound words are often not phrases with complete semantics; TextRank can only extract single words, but cannot extract combinations Words, leading to the keywords extracted by the TextRank algorithm are too generalized to express clear semantics

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