{"url":"/dataset/dr-inventor","name":"DRI Corpus","full_name":"Dr. Inventor Multi-layer Scientific Corpus","description_markdown":"The **Dr. Inventor Multi-Layer Scientific Corpus** (**DRI Corpus**) includes 40 Computer Graphics papers, selected by domain experts. Each paper of the Corpus has been annotated by three annotators by providing the following layers of annotations, each one characterizing a core aspect of scientific publications:\r\n\r\n* Scientific discourse: each sentence has been associated to a specific scientific discourse category (Background, Approach, Challenge, Future Work, etc.).\r\n* Subjective statements and novelty: each sentence has been characterized with respect to advantages, disadvantages and novel aspects presented.\r\n* Citation purpose: to each citation has been associated a purpose specifying the reason why the authors of the paper cited the specific piece of research.\r\n* Summary relevance of sentences and hand written summaries: each sentence of the paper has been characterized by an integer score ranging from 1 to 5, to point out the relevance of the same sentence for its inclusion in the summary of the paper. Sentences rated as 5 are the most relevant ones to summarize a paper. For each paper three hand-written summaries (max 250 words) are provided.\r\n\r\nSource: [Dr. Inventor Multi-layer Scientific Corpus](http://sempub.taln.upf.edu/dricorpus)","description_withheld":null,"homepage":"http://sempub.taln.upf.edu/dricorpus","introduced_date":"2016-05-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-multi-layered-annotated-corpus-of","title":"A Multi-Layered Annotated Corpus of Scientific Papers","first_author":"Beatriz Fisas","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Link Prediction","url":"/task/link-prediction","datasets_with_task":"/datasets/task/link-prediction"},{"name":"Relation Classification","url":"/task/relation-classification","datasets_with_task":"/datasets/task/relation-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["DRI Corpus"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/link-prediction-on-dr-inventor","task":"Link Prediction","dataset_variant":"DRI Corpus","rows":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"ResAttArg","paper":"/paper/multi-task-attentive-residual-networks-for","metrics":{"F1":"43.66"},"code_links":[{"title":"AGalassi/StructurePrediction18","url":"https://github.com/AGalassi/StructurePrediction18"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/relation-classification-on-dr-inventor","task":"Relation Classification","dataset_variant":"DRI Corpus","rows":1,"metrics":["Macro F1"],"first_row_in_archive_order":{"model":"ResAttArg","paper":"/paper/multi-task-attentive-residual-networks-for","metrics":{"Macro F1":"37.72"},"code_links":[{"title":"AGalassi/StructurePrediction18","url":"https://github.com/AGalassi/StructurePrediction18"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/multi-task-attentive-residual-networks-for","title":"Multi-Task Attentive Residual Networks for Argument Mining","date":"2021-02-24","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}