9 results about "Author name" patented technology

The invention discloses an automatic author name disambiguation method based on micro feature selection, which is used for solving the disambiguation problem caused by the fact that names have the same name and are different from those of the same person or different names and the same person in a current literature database, and realizes end-to-end differentiable feature selection optimization by designing a feature search space and adopting a Gumbel-Softmax technology. The method not only can automatically determine the optimal feature set, but also improves the adaptability of the model to the disambiguation task through the joint learning feature selection strategy and the similarity prediction model. Compared with a traditional feature engineering method based on experience and heuristic, the accuracy and robustness of the disambiguation model are remarkably improved while manual intervention and design errors are reduced, and the method can be widely applied to the fields of academic publishing, knowledge graph construction, scientific research achievement tracking and the like and has a wide application prospect. The method can help to improve the retrieval efficiency and accuracy of the literature database, provides more accurate academic resource matching for scientific researchers, and has a wide application prospect.

The invention discloses an author name disambiguation and error allocation detection method based on a large language model, and relates to the technical field of allocation detection. The method comprises the following steps: S1, forming a standardized academic literature data set; s2, generating a high-dimensional semantic feature vector representation used for representing a context semantic relationship of each piece of text information; s3, forming a uniform author identity representation vector; s4, utilizing the original academic literature data set to construct a multivariate relation graph of academic literature reference, authors and academic literatures, authors and institutions and copyright relation; s5, generating a preliminary author identity matching result; s6, generating an author identity probability distribution prediction result; s7, generating optimized author identity matching data; and S8, automatically correcting the author identity matching data by using a backward reasoning strategy to form a final author identity affiliation result. The signature variant identification capability is improved, and identity misjudgment caused by different expressions is avoided.

The application discloses an author name disambiguation method and related device. In the method, author information to be analyzed and a candidate information set are acquired; multiple dimension similarity mining is respectively performed between multiple pieces of author information in the candidate information set and the author information to be analyzed; a cooperation network is one of the multiple dimensions. The multiple dimension similarity mining results between the same piece of author information in the candidate information set and the author information to be analyzed are fused to obtain multiple dimension similarity fusion results, and a matching result of the author to be analyzed and an author in the candidate information set is obtained according to the multiple dimension similarity fusion results. The scheme improves the effectiveness of the cooperation network in author name disambiguation, realizes deep understanding of structural topological characteristics and dynamic grasping of network evolution mode, has higher accuracy than the prior art, and can further improve the accuracy of author name disambiguation.

The application provides a code statistics method and device, electronic equipment and storage medium, the method comprises: obtaining project information of a plurality of projects under a plurality of hierarchical groups to be counted; filtering out effective branches through the project information and a preset branch list, and extracting a plurality of code submission records carrying submission hash values from the effective branches; using the submission hash values to deduplicate the plurality of code submission records, filtering out effective code submission records in the deduplicated code submission records, and identifying author names to obtain garbled author names and normal author names; grouping the effective code submission records by email addresses, repairing garbled author names in the groups by using normal author names in the groups. Identify the main author and non-main author names in the group; add the code modification information of the non-main author names in each group to the main author names, and obtain the code statistics contribution of the main author names corresponding to each group, so as to improve the accuracy of code statistics.

The present invention discloses a method for author name disambiguation and detection of incorrect assignments based on large language models, which relates to the technical field of assignment detection. The method includes the following steps: S1. Form a standardized academic literature data set; S2. Generate high-dimensional semantic feature vector representations for characterizing the context semantic relationships of each text information; S3. Form a unified author identity characterization vector; S4. Use the original academic literature data set to construct a multi-relationship graph of academic literature citations, authors and academic literature, authors and institutions, and co-authorship relationships; S5. Generate preliminary author identity matching results; S6. Generate author identity probability distribution prediction results; S7. Generate optimized author identity matching data; S8. Use the reverse inference strategy to automatically correct the author identity matching data to form the final author identity attribution result. The present invention improves the ability to identify signature variants and avoids misjudgment of identity caused by different expressions.

This invention provides a method, application, and electronic device for constructing an author name disambiguation model. First, a name block and an initial training set consisting of entity pair training samples are constructed. An author name disambiguation baseline model is trained based on the initial training set. The baseline model is then used to calculate the global feature importance vector for each entity pair training sample through feature attribution calculation. The score for each entity pair training sample is calculated based on the global feature importance vector and a non-empty indicator vector, and a training set is selected. The baseline model is then retrained using the selected training set to obtain the author name disambiguation model. The objective loss function of the author name disambiguation model includes classification loss and attribution prior regularization, improving model interpretability, reducing information loss due to metadata sparsity, suppressing model attention drift, and being compatible with various model structures. It has low engineering modification costs and can be widely applied to name disambiguation scenarios in various academic data processing systems.

The invention discloses an author name disambiguation method and a related device. The method comprises the following steps: acquiring author information to be analyzed and a candidate information set; performing multi-dimensional similarity mining on the plurality of pieces of author information in the candidate information set and the author information to be analyzed; the cooperative network is one of a plurality of dimensions. And performing fusion processing on a multi-dimensional similarity mining result between the same author information in the candidate information set and the author information to be analyzed to obtain a multi-dimensional similarity fusion result, and obtaining a matching result of the author to be analyzed and the authors in the candidate information set according to the multi-dimensional similarity fusion result. According to the scheme, the effectiveness of the cooperation network in the author name disambiguation aspect is improved, deep understanding of structural topological characteristics and dynamic mastering of a network evolution mode are achieved, compared with the prior art, the author name disambiguation method has the advantage of higher accuracy, and author name disambiguation accuracy can be further improved.

The invention discloses a method, a system, equipment and a medium for checking words in a book certificate of a Chinese dictionary. The method comprises the following steps of: obtaining words, pinyin, an original document and segmented book certificate contents from preprocessed document data; standardizing the dynasty name, the author name and the literature name by replacing and / or clearing redundant symbols; searching word items in the content of the certificate, and marking unmatched results; performing multi-version retrieval in a traditional or simplified form on the word according to the dictionary type; screening non-book-certificate texts based on predefined keywords, and clearing a misinformation result; and outputting the remaining unmatched words and associated data as a manuscript review result. According to the embodiment of the invention, the problems of low efficiency and high error rate of manual proofreading are solved.

The invention discloses an author name disambiguation method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: acquiring and preprocessing a paper data set; blocking processing according to name fingerprints; performing first-stage merging based on same mechanism and text similarity; second-stage cross-mechanism judgment is carried out based on bidirectional partner coverage, time cross constraint and mechanism transfer prior; executing constrained graph clustering; according to the method, the disambiguation accuracy and recall rate of the authors with the same name can be improved under the condition that only abstracts and metadata exist, the situation that the authors replace work units can be further accurately recognized, and the method is suitable for scenes such as academic literature retrieval and researchers evaluation.