Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (2)

Search Parameters:
Keywords = semi-structured patent document

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
16 pages, 2150 KB  
Article
A New Entity Relationship Extraction Method for Semi-Structured Patent Documents
by Liyuan Zhang, Xiangyu Sun, Xianghua Ma and Kaitao Hu
Electronics 2024, 13(16), 3144; https://doi.org/10.3390/electronics13163144 - 8 Aug 2024
Cited by 1 | Viewed by 1953
Abstract
Aimed at mitigating the limitations of the existing document entity relation extraction methods, especially the complex information interaction between different entities in the document and the poor effect of entity relation classification, according to the semi-structured characteristics of patent document data, a patent [...] Read more.
Aimed at mitigating the limitations of the existing document entity relation extraction methods, especially the complex information interaction between different entities in the document and the poor effect of entity relation classification, according to the semi-structured characteristics of patent document data, a patent document ontology model construction method based on hierarchical clustering and association rules was proposed to describe the entities and their relations in the patent document, dubbed as MPreA. Combined with statistical learning and deep learning algorithms, the pre-trained model of the attention mechanism was fused to realize the effective extraction of entity relations. The results of the numerical simulation show that, compared with the traditional methods, our proposed method has achieved significant improvement in solving the problem of insufficient contextual information, and provides a more effective solution for patent document entity relation extraction. Full article
(This article belongs to the Section Artificial Intelligence)
Show Figures

Figure 1

21 pages, 777 KB  
Review
The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining
by Nícia Rosário-Ferreira, Catarina Marques-Pereira, Manuel Pires, Daniel Ramalhão, Nádia Pereira, Victor Guimarães, Vítor Santos Costa and Irina Sousa Moreira
BioChem 2021, 1(2), 60-80; https://doi.org/10.3390/biochem1020007 - 27 Jul 2021
Cited by 8 | Viewed by 6040
Abstract
Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical [...] Read more.
Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category. Full article
(This article belongs to the Special Issue Computational Analysis of Proteomes and Genomes)
Show Figures

Figure 1

Back to TopTop