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| # DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science | |
| ## About | |
| This repository contains the official implementation of | |
| [DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science](https://comingsoon). This model is trained on **4,408 distantly supervised tables** published in materials science research papers to extract compositions reported in the tables. These tables yielded a total of 38,799 tuples in the training set. | |
| The tuples are of the form $\{(id, c_k^{id}, p_k^{id}, u_k^{id} )\}_{k=1}^{K^{id}}$, where | |
| - $id$ is the id of the material composition reported in the tables | |
| - $c_k^{id}$ is the k-th chemical element in the material composition | |
| - $p_k^{id}$ is the percentage of k-th chemical element in the material composition | |
| - $u_k^{id}$ is the unit of the of $p_k^{id}$ (either mole % or weight %) | |
| The following figure represents the architecture of the model proposed in our work. | |
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| ## Notes | |
| - The [**code**](code) directory contains the file for training models reported in this paper. | |
| - The [**data**](data) directory contains the dataset for training models reported in this paper. | |
| - The [**notebooks**](notebooks) directory contains Jupyter notebook to visualise the dataset. | |
| - The respective directories and sub-directories contain task-specific **README** files. | |
| ## Citation | |
| If you find this repository useful, please cite our work as follows: | |
| ``` | |
| Incoming ACL 2023. Will be added soon. | |
| ``` | |