{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/discomat-distantly-supervised-composition","title":"DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles","arxiv_id":"2207.01079","date":"2022-07-03","proceeding":null,"authors":["Tanishq Gupta","Mohd Zaki","Devanshi Khatsuriya","Kausik Hira","N. M. Anoop Krishnan","Mausam"],"abstract":"A crucial component in the curation of KB for a scientific domain (e.g., materials science, foods & nutrition, fuels) is information extraction from tables in the domain's published research articles. To facilitate research in this direction, we define a novel NLP task of extracting compositions of materials (e.g., glasses) from tables in materials science papers. The task involves solving several challenges in concert, such as tables that mention compositions have highly varying structures; text in captions and full paper needs to be incorporated along with data in tables; and regular languages for numbers, chemical compounds and composition expressions must be integrated into the model. We release a training dataset comprising 4,408 distantly supervised tables, along with 1,475 manually annotated dev and test tables. We also present a strong baseline DISCOMAT, that combines multiple graph neural networks with several task-specific regular expressions, features, and constraints. We show that DISCOMAT outperforms recent table processing architectures by significant margins.","url_abs":"https://arxiv.org/abs/2207.01079v4","url_pdf":"https://arxiv.org/pdf/2207.01079v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"discomat-distantly-supervised-composition","repo_url":"https://github.com/m3rg-iitd/discomat","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"nutrition","task_name":"Nutrition"},{"task_slug":"table-extraction","task_name":"Table Extraction"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[{"slug":"information-extraction-from-tables","name":"Information Extraction from Tables","full_name":"Extraction materials compositions from tables of materials science research papers"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.01079","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01079"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/m3rg-iitd/discomat","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran_fixture":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"532e1cf084c5b0e9","entry":"process_mids","repo":"m3rg-iitd/discomat","repo_kind":"official","path":"code/evaluation/get_combined_results.py","file_url":"https://github.com/m3rg-iitd/discomat/blob/HEAD/code/evaluation/get_combined_results.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"532e1cf084c5b0e9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}