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Abstract generation method based on TMPP (Topic Model based on Phrase Parameter)

A model and abstract technology, applied in the field of abstract generation based on TMPP model, can solve the problem of low topic quality and reduce the accumulation of errors

Inactive Publication Date: 2016-08-31
SHANGHAI DIANJI UNIV
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Problems solved by technology

Therefore, the LDA model cannot be used directly for this problem. On the other hand, since the standard topic model LDA is an unsupervised topic model, the quality of the output topics is often not high, which requires some prior domains for the topic model. knowledge, guiding the model to generate better topics

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  • Abstract generation method based on TMPP (Topic Model based on Phrase Parameter)
  • Abstract generation method based on TMPP (Topic Model based on Phrase Parameter)
  • Abstract generation method based on TMPP (Topic Model based on Phrase Parameter)

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[0034] The implementation of the present invention is described below through specific examples and in conjunction with the accompanying drawings, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific examples, and various modifications and changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention.

[0035] In order to reduce the errors accumulated in the review mining process and generate better quality aspects, the present invention uses "bag of phrases" to represent reviews, and expands the parameter θ representing the document-topic in the standard LDA to a (aspect, rating) set. The topic model TMPP (Topic Model based on Phrase Parameter) for phrase parameter learning models aspect a...

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Abstract

The invention discloses an abstract generation method based on a TMPP (Topic Model based on Phrase Parameter). The abstract generation method is characterized in that a parameter [Theta] which shows document-topic in standard LDA (Latent Dirichlet Allocation) is expanded into an (aspect, rating) set, the TMPP is used for simultaneously modeling of the aspect and the rating, and a potential clustering variable c is introduced to show domain priori knowledge to guide the model to generate an aspect with better quality. The TMPP is adopted to generate an (aspect, rating) abstract, topic mining quality is guaranteed, an unsupervised learning way of the LDA is effectively overcome, and the phenomenon of the generation of meaningless topics is avoided.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a method for generating abstracts based on a TMPP model. Background technique [0002] At present, the standard topic model LDA (Latent Dirichlet Allocation) can realize the simultaneous prediction of a certain topic in online reviews and its related sentiment level. , rating) summary method. [0003] The standard topic model LDA often uses "word bags" to represent online review texts, and views reviews as a collection of potential topics, and each potential topic is regarded as a collection of word clusters, which provides a way to mine evaluated entities in online reviews. General method for summarization. But the research focus of this model is to classify the recognized aspects by sentiment, however, the goal of generating (aspect, rating) (aspect, rating) summary is to try to infer the aspect of the rated entity from the set of reviews of the same rated entity (aspect) and the ...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/35
Inventor 吕品钟忺
Owner SHANGHAI DIANJI UNIV
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