Short text similarity calculation method and system

Abstract

The invention provides a short text similarity calculation method. The method comprises the steps as follows: S1, performing word segmentation on training corpus, obtaining the word vector of each word with a word2vec algorithm, and combining the word vectors into a word vector set; S2, performing word segmentation on to-be-calculated short texts respectively, finding the word vector of each wordof the to-be-calculated short texts from the word vector set, and combining the word vectors into a short text vector set; S3, calculating the cosine similarity between each word vector in the word vector set and each word vector in the short text vector set, obtaining the maximum similarity value of each word vector, and combining the maximum similarity values into short text sentence vectors; S4, calculating the similarity between two short text sentence vectors to calculate the similarity between two short texts. The invention also provides a short text similarity calculation system. According to the similarity algorithm, short text sentences are represented by sentence vectors, the semantic similarity between short text sentences is effectively depicted, and the accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of short text similarity, and in particular relates to a short text similarity calculation method and system. Background technique [0002] With the rapid development of computer science and technology and the Internet, more and more data appear on the Internet in the form of short text, such as Weibo news, news headlines, posting comments, etc. Applying machine learning techniques such as classification and clustering to Internet short text data, digging out valuable information to provide useful convenience for people's lives, and to meet the needs of different aspects has become a very popular topic in big data application technology. However, short Chinese texts have the characteristics of sparse words, discrete semantics, and random words, which bring great challenges to the research of short Chinese texts. Therefore, it is necessary to mine short text data and accurately recognize its inner meaning It...

AI Technical Summary

Problems solved by technology

The analysis and processing of short texts in this way mainly has the following two problems. The first is that due to the sparsity of short text feature words, the text vectors are too sparse when using common text algorithms, resulting in clustering. The effect is not ideal, and the effect of long text cannot be achieved; the second is to use the vector space model to represent the text, only considering the statistical characteristics of words in the context, based on the assumption of linear independence between keywords, without considering the words themselves semantic information, so it has great limitations and cannot accurately express the inner semantic meaning of the sentence

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