Abstract
Aiming at the defects of traditional full-text retrieval models in dealing with mathematical expressions, which are special objects different from ordinary texts, a multimodal retrieval and ranking method for scientific documents based on hesitant fuzzy sets (HFS) and XLNet is proposed. This method integrates multimodal information, such as mathematical expression images and context text, as keywords to realize the retrieval of scientific documents. In the image modal, the images of mathematical expressions are recognized, and the hesitancy fuzzy set theory is introduced to calculate the hesitancy fuzzy similarity between mathematical query expressions and the mathematical expressions in candidate scientific documents. Meanwhile, in the text mode, XLNet is used to generate word vectors of the mathematical expression context to obtain the similarity between the query text and the mathematical expression context of the candidate scientific documents. Finally, the multimodal evaluation is integrated, and the hesitation fuzzy set is constructed at the document level to obtain the final scores of the scientific documents and corresponding ranked output. The experimental results show that the recall and precision of this method are 0.774 and 0.663 on the NTCIR dataset, respectively, and the average normalized discounted cumulative gain (NDCG) value of the top-10 ranking results is 0.880 on the Chinese scientific document (CSD) dataset.
Highlights
Scientific literature retrieval and ranking is an important way for workers to obtain scientific and technological information
This study proposes a multimodal retrieval method for scientific documents based on hesitant fuzzy sets (HFS) [21, 22] and XLNet [23]. is method integrates the functions of mathematical expression images and contextual text to improve the accuracy of retrieval results
Based on the retrieval and ranking mode of combining mathematical expression image and text, this study proposes a multimodal retrieval and ranking method for scientific documents based on HFS and XLNet. is method obtains the LaTeX structure information of mathematical expressions through image recognition algorithms and solves the single-modal problem of scientific document retrieval. e similarity between mathematical expressions is obtained by the evaluation of hesitant fuzzy sets, which solves the problem of the unity of evaluation of traditional mathematical expressions
Summary
Scientific literature retrieval and ranking is an important way for workers to obtain scientific and technological information. The single-modal retrieval model has great limitations because mathematical expressions in scientific documents often exist in multiple forms, such as embedding descriptions and images. E processing module of the image model is used to calculate the similarity between mathematical expression in images and in candidate technical documents. En, the hesitant fuzzy set theory is introduced to calculate the similarity between the mathematical expressions and the results are returned to the document processing module. E processing module of text modal is used to calculate the similarity between the mathematical expression context. E text in the context of mathematical expressions in the dataset is extracted and used to pretrain XLNet. XLNet is used to calculate the similarity between the query text and the mathematical expression context of the candidate scientific documents. E document processing module is used to output documents in order. e document attributes are designed, the scores of the documents are calculated by hesitation fuzzy set, and the ranking results are output in descending order of similarity
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