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A Laos language text subject classification method

A topic classification and text technology, applied in the fields of natural language processing and machine learning, can solve the problems of ignoring information, text misunderstanding, etc., to avoid zero probability problems, improve accuracy, and improve the effect of classification

Active Publication Date: 2019-02-01
KUNMING UNIV OF SCI & TECH
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  • Claims
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Problems solved by technology

But it has its own shortcomings, that is, it thinks that all feature attributes are conditionally independent, which is equivalent to putting text feature information into a word bag without considering the impact of the order of words, which often ignores a lot of information, misinterpreting the text

Method used

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  • A Laos language text subject classification method
  • A Laos language text subject classification method
  • A Laos language text subject classification method

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Embodiment 1

[0020] Embodiment 1: as Figure 1-2 Shown, a kind of Lao language text subject classification method, described method step is as follows: Step1, utilize web crawler technology to crawl Lao text, the text that has crawled five categories in total is respectively: economy, politics, education, tourism ,generally. Store them in the corresponding five folders. The folders are named after categories to facilitate subsequent retrieval and processing, and then perform text processing on the crawled articles to remove some noise words that have nothing to do with classification, so as to build a corpus; Further, the noise words can be set to include emoticons, numbers, spaces, and stop words; wherein emoticons, numbers, and spaces are removed by regular expressions, and stop words are removed by using a stop word table (appearing in the stop word table words are removed). When removing some unrelated noise words, the regular expression encoding is used as follows: u"^[\u0000-\u10ff...

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Abstract

The invention discloses a Laos language text subject classification method, belonging to the technical field of natural language processing and machine learning. The N-gram language feature extractionmodel and naive bayesian mathematical model to achieve the recognition of laos article theme, to some extent, eliminated the limitations of naive bayesian. It considers the conditional independence assumption that the text is regarded as a word bag model without considering the order information between words, and simultaneously uses the unigram and bigram feature model, which improves the recognition rate of the text.

Description

technical field [0001] The invention relates to a Lao language text topic classification method, which belongs to the technical fields of natural language processing and machine learning. Background technique [0002] With the popularization of the network, the information on the network increases exponentially. When users use search engines to retrieve the information they want, web pages often return thousands of relevant pages, and how can users quickly and effectively locate the desired information without viewing these pages one by one? At this time, topic recognition plays an important role. It can use our pre-trained classifier to locate the topic of the content that the user wants in the limited information input by the user, so as to respond effectively to the user. The Naive Bayesian classification model is a method with a long history and a solid theoretical foundation. It is a direct and efficient method for dealing with many problems at the same time, and many ...

Claims

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Application Information

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IPC IPC(8): G06F16/9535
Inventor 周兰江王兴金张建安周枫
Owner KUNMING UNIV OF SCI & TECH
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