pierresenellart / theoremkb

Collection of tools to extract semantic information from (mathematical) research articles

TheoremKB

TheoremKB is a research project and a corresponding collection of tools to extract semantic information from (mathematical) research articles. This is an ongoing project, with preliminary code available from this repository.

Bibliography

For a high-level overview of the project, see this set of slides.

For a more in-depth look at some of the aspects of the project, see:

• Lucas Pluvinage's Master Thesis on using style-based information for Extracting scientific results from research articles.
• Théo Delemazure's Master Thesis on first steps towards A Knowledge Base of Mathematical Results.
• Shrey Mishra's paper on Towards Extraction of Theorems and Proofs in Scholarly Articles comparing various techniques evaluated individually at a single line level.
• Yacine Brihmouche's Master's thesis on TheoremKB: a knowledge base of Mathematical results connecting proofs and theorems from different papers.
• Antoine Gauquier's Master's thesis on Impact of the document class in the automatic extraction of mathematical environments in the scientific literature
• Antoine Gauquier's paper on Automatically inferring the document class used in a scientific article.
• Shrey Mishra's paper on Multimodal Machine Learning for Extraction of Theorems and Proofs in the Scientific Literature.
• Shufan JIANG's paper on Extracting Definienda in Mathematical Scholarly Articles with Transformers.

Dataset

One of our dataset of reference is formed of 4400 articles extracted from arXiv, see arXiv Bulk Data Access for bulk access to the data. For licensing reasons, this datasets cannot be reshared, but we provide in Dataset/links.csv the reference to all articles of the dataset.

Tools

We are currently experimenting with the extraction of mathematical results based upon 3 approaches:

1. Using style-based information
2. Using Computer Vision based object detection to identify mathematical results

3. Using NLP based techniques such as transformers and LSTM networks for sequence prediction

Installation

For Computer Vision and NLP based extractions (Please follow the jupyter notebooks) in the directory /Computer_Vision and NLP

• Computer Vision notebooks

/Computer_Vision/1.1 Computer vision preprocessing.ipynb contains the preprocessing step and preparing the data into YOLO format /Computer_Vision/obj.data, /Computer_Vision/obj.names , /Computer_Vision/yolov4-obj.cfg contains the image annotations directory path, class labels and configuration file of the YOLO network trained

• NLP notebooks

/2.1 NLP text data preprocessing.ipynb contains the preprocessing step and preparing of the xml files /transformers_tkb.ipynb contains application of several AutoEncoding Transformers all base models (SciBert, Bert, DistilBert) /lstm_tkb_full.ipynb contains LSTM implementation on Full data /lstm_trimmed.ipynb contains LSTM implementation on imbalanced data

• Style based

See the instructions within the Styling directory.

Participants and contact

The project is led by Pierre Senellart, within the Valda research group joint between ENS, PSL University, CNRS and Inria.

The project has also involved:

• Théo Delemazure, Master's student, ENS
• Lucas Pluvinage, Master's student, ENS
• Shrey Mishra, PhD candidate, ENS
• Antoine Gauquier, PhD candidate, ENS
• Shufan JIANG, Postdoctoral Candidate, ENS

Contact Pierre Senellart for further information.

Funding

This work has been funded by the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute).

Pierre Senellart's work is also supported by his secondment to Institut Universitaire de France.