A Cross-lingual Information Retrieval Method Based on Concept Map

Abstract

The present invention discloses a cross-lingual information retrieval method based on a concept map. The cross-language information retrieval method based on a concept map is a method of a cross-language similarity evaluation framework CG-CLIR, which includes the following steps: S1, based on Skip-Gram The concept graph edge representation; S2, high-order semantic embedding and similarity calculation of fusion edge information; the step S1 includes text preprocessing, concept graph edge semantic embedding; the step S2 includes graph-level semantic embedding based on LSTM, based on Similarity Computation for Graph Embeddings. The method of the present invention overcomes language barriers in text cross-language information retrieval, and realizes the effect of semantic retrieval without translation.

Description

technical field [0001] The invention relates to cross-language information retrieval, in particular to a cross-language information retrieval method based on concept maps. Background technique [0002] At present, vector representation and processing of text has become the mainstream of text analysis tasks. The most common one is to vectorize the representation of words, such as the one-hot model and word embedding model, which represent n words in a sentence as n A d-dimensional vector, so that the sentence is an n*d-dimensional matrix, which is convenient for processing. Another method is to map sentences or documents into a vector, and construct paragraphs and texts into a matrix of vectors. When dealing with this method, more consideration is given to longer sequence information issues, and it can better represent global information. However, since the sentence is variable in length, and as the basic representation unit of semantics, words can have many different combi...

AI Technical Summary

Problems solved by technology

[0004] Traditional retrieval methods often use shallow text feature information to evaluate similarity. Even if semantics is used for similarity comparison, it is often only reflected at the word level, and the use of global phonetic features is not sufficient.

In the cross-language similarity retrieval task, due to the differences in the grammar of different languages, the translation of words also has the problem of disambiguation, and generally cannot be retrieved by simple text features.

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