![]() It is likely that certain content of journals of interest in a particular task is not distributed as a part of the Open Access subset.Ī long-standing promise of BioNLP has been to help accelerate the vital process of literature-based biocuration, where published information is carefully translated into the knowledge architecture of biomedical databases, using specific BioNLP tools. Many past biomedical text mining studies have used either the abstracts of scientific papers or relatively small collections of full-text articles sampled from the Open Access subset of PubMed Central. ![]() Given the ubiquity of the ‘Portable Document Format’ (PDF) as a means of distributing scientific publications and since access to information in full-text documents is vital for developing effective text-mining applications, it is essential to the general BioNLP community that developers of such applications can extract the textual content from PDF files accurately with open-source tools. NLP techniques such as Named Entity Recognition and Semantic Relation Extraction have been shown to be very useful to biologists studying protein-protein interactions and Gene-Disease-Phenotype relations. The field of Biomedical Natural Language Processing (BioNLP) is maturing, with specific fields of software development in response to user requirements: e.g., links between databases and literature, better tool interactivity and integration and the development of high-quality NLP resources. The release of the system is available at. LA-PDFText is an open-source tool for accurately extracting text from full-text scientific articles. Finally, we discuss preliminary error analysis for our system and identify further areas of improvement. We then compared this accuracy with that of the text extracted by the PDF2Text system, 2commonly used to extract text from PDF. Additionally, we have compared the accuracy of the text extracted by LA-PDFText to the text from the Open Access subset of PubMed Central. ![]() We also present an evaluation of the accuracy of the block detection algorithm used in step 2. We show that our system can identify text blocks and classify them into rhetorical categories with Precision 1 = 0.96% Recall = 0.89% and F1 = 0.91%. The system works in a three-stage process: (1) Detecting contiguous text blocks using spatial layout processing to locate and identify blocks of contiguous text, (2) Classifying text blocks into rhetorical categories using a rule-based method and (3) Stitching classified text blocks together in the correct order resulting in the extraction of text from section-wise grouped blocks. The LA-PDFText system focuses only on the textual content of the research articles and is meant as a baseline for further experiments into more advanced extraction methods that handle multi-modal content, such as images and graphs. Our paper describes the construction and performance of an open source system that extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. In this paper we introduce the ‘Layout-Aware PDF Text Extraction’ (LA-PDFText) system to facilitate accurate extraction of text from PDF files of research articles for use in text mining applications. The absence of effective means to extract text from these PDF files in a layout-aware manner presents a significant challenge for developers of biomedical text mining or biocuration informatics systems that use published literature as an information source. The Portable Document Format (PDF) is the most commonly used file format for online scientific publications.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |