grobid
paperetl
grobid | paperetl | |
---|---|---|
12 | 12 | |
3,120 | 319 | |
- | 5.3% | |
9.2 | 6.3 | |
13 days ago | 6 months ago | |
Java | Python | |
Apache License 2.0 | Apache License 2.0 |
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grobid
- FLaNK-AIM Weekly 06 May 2024
- Show HN: Open-source Rule-based PDF parser for RAG
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- Grobid β ML software for extracting information from scholarly documents
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How to create a web app that turns academic papers into text documents
Interesting concept. Grobid tries to do the same https://github.com/kermitt2/grobid
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Extract research paper`s references
I would suggest using grobid - a pipeline for extracting scientific PDFs into a common XML format which can be easily parsed. Grobid has quite a nice mature REST API that I've used in some of my own projects. It parses references and matches them to their DOI using the CrossRef API with a reported 95% F1 score. This should make your job pretty simple as far as I can tell - all you'd need to do is run your papers through grobid and then build a citation graph by comparing document DOIs.
- Free/open-source alternatives to Connected Papers...?
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Seeking Advice: How to extract Abstract from scientific journals (.pdfs) 10k+.
Just use science-parse or GROBID. They have been designed for that exact reason.
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Project to rebuild papers with plaintext markup languages
- I ended up using Grobid, which converts the PDF to a very detailed XML format. The format is not a word processing format though, but a format specifically for representing scientific documents. I don't know, if it would, for example, contain tags about bold or italicized text. The tool is working really well, but since you probably cannot use the output XML format directly, it will need some postprocessing, which would be relatively simple with XML parsing libraries.
paperetl
- Show HN: Open-source Rule-based PDF parser for RAG
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Oracle of Zotero: LLM QA of Your Research Library
Nice project!
I've spent quite a lot of time in the medical/scientific literature space. With regards to LLMs, specifically RAG, how the data is chunked is quite important. With that, I have a couple projects that might be beneficial additions.
paperetl (https://github.com/neuml/paperetl) - supports parsing arXiv, PubMed and integrates with GROBID to handle parsing metadata and text from arbitrary papers.
paperai (https://github.com/neuml/paperai) - builds embeddings databases of medical/scientific papers. Supports LLM prompting, semantic workflows and vector search. Built with txtai (https://github.com/neuml/txtai).
While arbitrary chunking/splitting can work, I've found that integrating parsing that has knowledge of medical/scientific paper structure increases the overall accuracy and experience of downstream applications.
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[P] Parse research papers into structured data
paperai | paperetl
- Parse research papers into a structured dataset
- ETL for medical and scientific papers
- Show HN: ETL for Medical and Scientific Papers
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Seeking Advice: How to extract Abstract from scientific journals (.pdfs) 10k+.
paperai and paperetl are a set of projects to consider for this task.
- paperetl: ETL processes for medical and scientific papers
What are some alternatives?
Parsr - Transforms PDF, Documents and Images into Enriched Structured Data
SciencePlots - Matplotlib styles for scientific plotting
CERMINE - Content ExtRactor and MINEr
tika-python - Tika-Python is a Python binding to the Apache Tikaβ’ REST services allowing Tika to be called natively in the Python community.
Smile - Statistical Machine Intelligence & Learning Engine
ciscoconfparse - Parse, Audit, Query, Build, and Modify Cisco IOS-style configurations.
science-parse - Science Parse parses scientific papers (in PDF form) and returns them in structured form.
paperai - π π€ Semantic search and workflows for medical/scientific papers
datahub - The Metadata Platform for your Data Stack
rdm - Our regulatory documentation manager. Streamlines 62304, 14971, and 510(k) documentation for software projects.
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
dagster - An orchestration platform for the development, production, and observation of data assets.