Spatial semantic similarity calculation method based on sliding window sampling

A technology of semantic similarity and sliding window, which is applied in computing, instrumentation, text database query, etc., and can solve problems such as insufficient accuracy
CN110990724AActive Publication Date: 2020-04-10WUHAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
WUHAN UNIV
Publication Date
2020-04-10

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Abstract

The invention discloses a spatial semantic similarity calculation method based on sliding window sampling. The method comprises the following steps: preprocessing corpus data containing spatial information, performing projection processing on coordinates in the preprocessed corpus data by adopting a preset equal-area projection method to obtain an actual space range, determining a context window,performing sliding sampling, and finally performing similarity calculation on every two words in a word set of the whole corpus. According to the method, a model capable of measuring the spatial semantic similarity of the words is constructed by mining the spatial semantic similarity relationship between the words. The method is superior to a traditional text similarity model and a geographic space similarity model in the aspect of comprehensively considering spatial correlation and text correlation. As a new angle of understanding human natural language by integrating human spatial thinking and spatial perception, a traditional natural semantic similarity model is effectively supplemented, and the accuracy of an intelligent geographic information retrieval and recommendation system is effectively improved.
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Description

technical field

[0001] The invention relates to the technical field of geographic information retrieval, in particular to a method for calculating spatial semantic similarity based on sliding window sampling. Background technique

[0002] For natural language processing (NLP) problems under the current cross-discipline of computer and linguistics, calculating the similarity relationship between words in text is a key part of solving these problems.

[0003] In the prior art, general word similarity models are obtained by using large text corpus and deep learning training methods, such as Google's Word2Vec (Mikolov, Chen et al.2013) model and Facebook's Fasttext (Joulin, Grave et al. .2016) model.

[0004] In the process of implementing the present invention, the inventor of the present application found that the method of the prior art has at least the following technical problems:

[0005] The above-mentioned models in the prior art perform well on common general texts, b...

Claims

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