Data mining technology is revolutionizing the way we analyze scientific documents. With the rapid growth of the internet, it has become crucial to extract valuable information from vast resources efficiently, particularly in the context of research surveys and comprehension. In this article, we will delve into the significance of data extraction techniques for constructing foundational data and understanding the physical structure of scientific documents.
Section 1: Understanding Data Extraction Techniques
Data extraction techniques are fundamental approaches to classify and identify information within scientific articles. These techniques include rule-based (RB) or machine learning (ML) methods, which offer different advantages and disadvantages. RB methods can incur high coding costs for articles with intricate typesetting, while ML methods rely solely on annotation work for complex content types.
Section 2: Block Coordinate and Boundary Coordinate
In scientific documents, the text is distributed more regularly, making it essential to recognize the supplement information blocks. To accomplish this, we must understand the width and height characteristics of the blocks. By setting the largest width/height as the standard for all blocks, dividing the width/height of each block, and encoding it, we can efficiently extract the necessary information.
Section 3: Font Type (ft)
The font type (ft) is an essential characteristic that helps in recognizing the supplement information blocks. By analyzing the font type, we can identify the relevant information and categorize it accordingly.
Methodology-Implementation
Our proposed methodology involves two phases: Preprocessing and Postprocessing.
Phase 1: Preprocessing
4.1.1 Text Block Parsing
Scientific repositories currently store research articles in PDF format, which contains text blocks that need to be parsed. To automate this process, we utilize the external library pymupdf [22] in Python. By analyzing the raw structure of scientific documents, we can use the characteristics of line and column spacing to divide the text into multiple sub-text blocks.
4.1.2 Extracting Accompanying Information from Text
Once we have parsed the text blocks, we need to extract accompanying information such as font type (ft), which helps in recognizing supplementary information blocks. This step is crucial in capturing the essence of the article without oversimplifying it.
Phase 2: Postprocessing
4.2.1 Mining and Analyzing Data
After preprocessing, we mine and analyze the extracted data to identify patterns and relationships. This phase helps us understand the physical structure of scientific documents and how they are organized. By analyzing the data, we can identify the most relevant information and categorize it accordingly.
Conclusion
In conclusion, data mining technology is essential for constructing foundational data for research articles. By understanding the physical structure of scientific documents and using data extraction techniques, we can efficiently collect and organize the language text elements (main text, itemized form, sections, footnotes) and non-language text elements (figures, tables, formulas, quotation marks). Our proposed methodology involves two phases: preprocessing and postprocessing. By following this approach, researchers can demystify complex concepts by using everyday language and engaging metaphors or analogies to create a concise summary of the article in 1000 words or less, targeting the comprehension level of an average adult.
