Ambiguity elimination method and apparatus for e-commerce product comment vocabularies

A product review and vocabulary technology, applied in semantic analysis, electronic digital data processing, special data processing applications, etc., can solve the problems of inaccurate word meaning judgment and large randomness of e-commerce product review vocabulary, and achieve the effect of eliminating ambiguity

Active Publication Date: 2017-12-29
美云智数科技有限公司
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Problems solved by technology

[0006] In order to solve the problem of large randomness and inaccurate determination of word meanings in e-commerce product ...

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[0032] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with aspects of the invention as recited in the appended claims.

[0033] figure 1 is a schematic diagram of an implementation environment involved in accordance with the present disclosure. The implementation environment includes: at least one mobile terminal 110 and a server 120 .

[0034] The association manner between the mobile terminal 110 and the server 120 includes a hardware network association manner and / or protocol, and a data association manner betwee...

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Abstract

The invention discloses an ambiguity elimination method and apparatus for e-commerce product comment vocabularies. The method comprises the steps of obtaining a historical comment text, and extracting historical feature words and historical sentiment words matched with the historical feature words from the historical comment text; according to a co-occurrence relationship between the historical feature words and the historical sentiment words corresponding to the historical feature words, screening out a combination of the historical feature words and the historical sentiment words, which occur most frequently; according to the combination of the historical feature words and the historical sentiment words, which occur most frequently, generating meaning item annotations of the historical feature words; obtaining a new comment text, and extracting a combination of new feature words and corresponding new sentiment words from the new comment text; and according to the combination of the new feature words and the new sentiment words, querying the combination of the historical feature words and the historical sentiment words, matched with the combination of the new feature words and the new sentiment words, and taking the meaning item annotations of the matched historical feature words as word meanings of the new feature words. According to the technical scheme, word meaning analysis and meaning item determination of the product comment vocabularies are realized, so that accurate paraphrases of product feature words occurring in contexts are accurately judged in different comment contexts.

Description

technical field [0001] The present disclosure relates to the technical field of natural language processing, in particular to a method and device for disambiguating product review vocabulary. Background technique [0002] Polysemy is an inherent feature of natural language. When using computers for natural language processing, this ambiguity has a great impact on the analysis results. The study found that the specific meaning of a term mainly depends on the context, that is, the context in which a word appears is the factor that determines the meaning of a word. [0003] The word sense disambiguation technology based on statistical learning uses corpus as the knowledge source, and learns various word sense features from labeled or unlabeled corpus. By calculating the probability weight of the words in the given text in the context, the word meaning with the largest probability weight is selected as the best result output, such as Bayesian classifier, maximum entropy classi...

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Application Information

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IPC IPC(8): G06F17/27G06F17/30
CPCG06F16/30G06F40/253G06F40/30
Inventor 谷云松黄侃于英
Owner 美云智数科技有限公司
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