A Passage-Level Text Similarity Calculation

Liu, Ming; Zheng, Zihao; Qin, Bing; Liu, Yitong

doi:10.1007/978-3-030-60450-9_17

Cited by 1 publication

(1 citation statement)

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“…With the continuous development of machine learning technology, the application of text similarity matching algorithm in Chinese text has also been pushed to a new height [9][10][11][12]. Shancheng Tang et al proposed a Chinese short text sequence similarity model based on LSTM, and used the Chinese semantic similarity data set designed by experts for training, so as to overcome the polysemy and semantic ambiguity of Chinese text to some extent [13].…”

Section: Introductionmentioning

confidence: 99%

A multi-dimensional fusion strategy similarity measure method for patent application technology disclosure document

Zhu,

Li,

Hou

et al. 2023

PLoS ONE

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Patent application technology disclosure document is one of the important bases for judging patent novelty and uniqueness. Automated evaluation can effectively solve the problems of long time and strong subjectivity of human evaluation. The text similarity evaluation algorithm based on corpus and deep learning technology has problems such as insufficient amount of cross-library learning data and insufficient core content tendency in the similarity judgment of patent application technology disclosure document, which limits their performance and practical application. In this paper, we propose a similarity evaluation method of patent application technology disclosure document based on multi-dimensional fusion strategy to realize the similarity measurement of patents. Firstly, in the text preprocessing section, word segmentation reconstruction and similarity evaluation optimization strategies based on word frequency and part-of-speech score weighted fusion are proposed. Then, a similarity calculation method of patent application technology disclosure document based on two new mapping spaces of dot matrix and image is proposed to achieve a more diversified comprehensive evaluation. The algorithm was evaluated by using four published text similarity matching datasets (containing 0–5 or 0/1 labels) and a set of patent application technology disclosure documents. Experimental results show that on the published text similarity matching datasets, the similarity evaluation method under the multi-dimensional fusion strategy proposed in this paper has a discrimination accuracy improvement of about 10% compared to traditional vector semantic model, and can match the discriminative ability of lightweight deep learning models without the need for training. At the same time, the discrimination accuracy of the proposed method on the sample dataset of patent application technology disclosure document is superior to traditional vector semantic model (20%) and various deep learning models (1%-8%), and the precision and recall rate are relatively balanced. The visual analysis results on the dataset of the patent application technology disclosure documents also prove the effectiveness and reliability of the similarity calculation method proposed in the dot matrix and image space, which provide a new idea and method for the similarity evaluation between patent application technology disclosure document.

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