The New York Times Annotated Corpus contains over 1.8 million articles written and published by the New York Times between January 1, 1987 and June 19, 2007 with article metadata provided by the New York Times Newsroom, the New York Times Indexing Service and the online production staff at nytimes.com. The corpus includes:
264 PAPERS • 7 BENCHMARKS
TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC KBP challenges and crowdsourcing.
185 PAPERS • 2 BENCHMARKS
The CoNLL dataset is a widely used resource in the field of natural language processing (NLP). The term “CoNLL” stands for Conference on Natural Language Learning. It originates from a series of shared tasks organized at the Conferences of Natural Language Learning.
176 PAPERS • 49 BENCHMARKS
The FewRel (Few-Shot Relation Classification Dataset) contains 100 relations and 70,000 instances from Wikipedia. The dataset is divided into three subsets: training set (64 relations), validation set (16 relations) and test set (20 relations).
170 PAPERS • 4 BENCHMARKS
DocRED (Document-Level Relation Extraction Dataset) is a relation extraction dataset constructed from Wikipedia and Wikidata. Each document in the dataset is human-annotated with named entity mentions, coreference information, intra- and inter-sentence relations, and supporting evidence. DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. Along with the human-annotated data, the dataset provides large-scale distantly supervised data.
144 PAPERS • 4 BENCHMARKS
The WebNLG corpus comprises of sets of triplets describing facts (entities and relations between them) and the corresponding facts in form of natural language text. The corpus contains sets with up to 7 triplets each along with one or more reference texts for each set. The test set is split into two parts: seen, containing inputs created for entities and relations belonging to DBpedia categories that were seen in the training data, and unseen, containing inputs extracted for entities and relations belonging to 5 unseen categories.
143 PAPERS • 17 BENCHMARKS
Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking.
142 PAPERS • 3 BENCHMARKS
The BLUE benchmark consists of five different biomedicine text-mining tasks with ten corpora. These tasks cover a diverse range of text genres (biomedical literature and clinical notes), dataset sizes, and degrees of difficulty and, more importantly, highlight common biomedicine text-mining challenges.
122 PAPERS • NO BENCHMARKS YET
SciERC dataset is a collection of 500 scientific abstract annotated with scientific entities, their relations, and coreference clusters. The abstracts are taken from 12 AI conference/workshop proceedings in four AI communities, from the Semantic Scholar Corpus. SciERC extends previous datasets in scientific articles SemEval 2017 Task 10 and SemEval 2018 Task 7 by extending entity types, relation types, relation coverage, and adding cross-sentence relations using coreference links.
117 PAPERS • 7 BENCHMARKS
The dataset for the SemEval-2010 Task 8 is a dataset for multi-way classification of mutually exclusive semantic relations between pairs of nominals.
117 PAPERS • 1 BENCHMARK
A dataset of large scale alignments between Wikipedia abstracts and Wikidata triples. T-REx consists of 11 million triples aligned with 3.09 million Wikipedia abstracts (6.2 million sentences).
107 PAPERS • 2 BENCHMARKS
ACE 2005 Multilingual Training Corpus contains the complete set of English, Arabic and Chinese training data for the 2005 Automatic Content Extraction (ACE) technology evaluation. The corpus consists of data of various types annotated for entities, relations and events by the Linguistic Data Consortium (LDC) with support from the ACE Program and additional assistance from LDC.
62 PAPERS • 9 BENCHMARKS
The Re-TACRED dataset is a significantly improved version of the TACRED dataset for relation extraction. Using new crowd-sourced labels, Re-TACRED prunes poorly annotated sentences and addresses TACRED relation definition ambiguity, ultimately correcting 23.9% of TACRED labels. This dataset contains over 91 thousand sentences spread across 40 relations. Dataset presented at AAAI 2021.
47 PAPERS • 1 BENCHMARK
ACE 2004 Multilingual Training Corpus contains the complete set of English, Arabic and Chinese training data for the 2004 Automatic Content Extraction (ACE) technology evaluation. The corpus consists of data of various types annotated for entities and relations and was created by Linguistic Data Consortium with support from the ACE Program, with additional assistance from the DARPA TIDES (Translingual Information Detection, Extraction and Summarization) Program. The objective of the ACE program is to develop automatic content extraction technology to support automatic processing of human language in text form. In September 2004, sites were evaluated on system performance in six areas: Entity Detection and Recognition (EDR), Entity Mention Detection (EMD), EDR Co-reference, Relation Detection and Recognition (RDR), Relation Mention Detection (RMD), and RDR given reference entities. All tasks were evaluated in three languages: English, Chinese and Arabic.
46 PAPERS • 5 BENCHMARKS
The DDIExtraction 2013 task relies on the DDI corpus which contains MedLine abstracts on drug-drug interactions as well as documents describing drug-drug interactions from the DrugBank database.
45 PAPERS • 3 BENCHMARKS
QA-SRL was proposed as an open schema for semantic roles, in which the relation between an argument and a predicate is expressed as a natural-language question containing the predicate (“Where was someone educated?”) whose answer is the argument (“Princeton”). The authors collected about 19,000 question-answer pairs from 3,200 sentences.
41 PAPERS • NO BENCHMARKS YET
A more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-above (NOTA) relations?
38 PAPERS • NO BENCHMARKS YET
RadGraph is a dataset of entities and relations in radiology reports based on our novel information extraction schema, consisting of 600 reports with 30K radiologist annotations and 221K reports with 10.5M automatically generated annotations.
37 PAPERS • NO BENCHMARKS YET
The Re-DocRED Dataset resolved the following problems of DocRED:
21 PAPERS • 2 BENCHMARKS
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain unambiguous annotations for all datasets. Furthermore, we build an evaluation system and carefully choose evaluations metrics for every task, thus establishing fair comparison across Korean language models.
19 PAPERS • 1 BENCHMARK
2010 i2b2/VA is a biomedical dataset for relation classification and entity typing.
18 PAPERS • 4 BENCHMARKS
JNLPBA is a biomedical dataset that comes from the GENIA version 3.02 corpus (Kim et al., 2003). It was created with a controlled search on MEDLINE. From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. 36 terminal classes were used to annotate the GENIA corpus.
18 PAPERS • 2 BENCHMARKS
The CoNLL04 dataset is a benchmark dataset used for relation extraction tasks. It contains 1,437 sentences, each of which has at least one relation. The sentences are annotated with information about entities and their corresponding relation types.
17 PAPERS • 3 BENCHMARKS
The 'Deutsche Welle corpus for Information Extraction' (DWIE) is a multi-task dataset that combines four main Information Extraction (IE) annotation sub-tasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document.
17 PAPERS • 4 BENCHMARKS
ChemProt consists of 1,820 PubMed abstracts with chemical-protein interactions annotated by domain experts and was used in the BioCreative VI text mining chemical-protein interactions shared task.
16 PAPERS • 1 BENCHMARK
XFUND is a multilingual form understanding benchmark dataset that includes human-labeled forms with key-value pairs in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese).
15 PAPERS • NO BENCHMARKS YET
BioRED is a first-of-its-kind biomedical relation extraction dataset with multiple entity types (e.g. gene/protein, disease, chemical) and relation pairs (e.g. gene–disease; chemical–chemical) at the document level, on a set of600 PubMed abstracts. Furthermore, BioRED label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information.
14 PAPERS • 3 BENCHMARKS
A SemEval shared task in which participants must extract definitions from free text using a term-definition pair corpus that reflects the complex reality of definitions in natural language.
14 PAPERS • NO BENCHMARKS YET
Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports.
13 PAPERS • 3 BENCHMARKS
Preprocessed version of NYT11.
13 PAPERS • 1 BENCHMARK
The BioCreative V CDR task corpus is manually annotated for chemicals, diseases and chemical-induced disease (CID) relations. It contains the titles and abstracts of 1500 PubMed articles and is split into equally sized train, validation and test sets. It is common to first tune a model on the validation set and then train on the combination of the train and validation sets before evaluating on the test set. It is also common to filter negative relations with disease entities that are hypernyms of a corresponding true relations disease entity within the same abstract (see Appendix C of this paper for details).
11 PAPERS • 2 BENCHMARKS
The gene-disease associations corpus contains 30,192 titles and abstracts from PubMed articles that have been automatically labelled for genes, diseases and gene-disease associations via distant supervision. The test set is comprised of 1000 of these examples. It is common to hold out a random 20% of the examples in the train set as a validation set.
10 PAPERS • 2 BENCHMARKS
The Sixth Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing Challenge for Clinical Records focused on the temporal relations in clinical narratives. The organizers provided the research community with a corpus of discharge summaries annotated with temporal information, to be used for the development and evaluation of temporal reasoning systems. 18 teams from around the world participated in the challenge. During the workshop, participating teams presented comprehensive reviews and analysis of their systems, and outlined future research directions suggested by the challenge contributions.
9 PAPERS • 2 BENCHMARKS
Abstract Objective This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evaluated 3 tasks: concept extraction, relation classification, and end-to-end systems. We perform an analysis of the results to identify the state of the art in these tasks, learn from it, and build on it.
7 PAPERS • NO BENCHMARKS YET
MAVEN-ERE is a dataset designed for event relation extraction tasks containing 103,193 event coreference chains, 1,216,217 temporal relations, 57,992 causal relations, and 15,841 subevent relations.
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).
6 PAPERS • NO BENCHMARKS YET
Phenotype-Gene Relations (PGR) is a corpus that consists of 1712 abstracts, 5676 human phenotype annotations, 13835 gene annotations, and 4283 relations.
6 PAPERS • 1 BENCHMARK
The training and development dataset for our task was taken from previous work on wet lab corpus (Kulkarni et al., 2018) that consists of from the 623 protocols. We excluded the eight duplicate protocols from this dataset and then re-annotated the 615 unique protocols in BRAT (Stenetorp et al., 2012).
6 PAPERS • 2 BENCHMARKS
FinRED is a relation extraction dataset curated from financial news and earning call transcripts containing relations from the finance domain. FinRED has been created by mapping Wikidata triplets using distance supervision method.
5 PAPERS • NO BENCHMARKS YET
GAD, or Gene Associations Database, is a corpus of gene-disease associations curated from genetic association studies.
5 PAPERS • 1 BENCHMARK
HyperRED is a dataset for the new task of hyper-relational extraction, which extracts relation triplets together with qualifier information such as time, quantity or location. For example, the relation triplet (Leonard Parker, Educated At, Harvard University) can be factually enriched by including the qualifier (End Time, 1967). HyperRED contains 44k sentences with 62 relation types and 44 qualifier types.
5 PAPERS • 4 BENCHMARKS
LabPics Chemistry Dataset
The TACRED-Revisited dataset improves the crowd-sourced TACRED dataset for relation extraction by relabeling the dev and test sets using expert linguistic annotators. Relabeling focuses on the 5K most challenging instances in dev and test, in total, 51.2% of these are corrected. Published at ACL 2020.
CrossRE is a cross-domain benchmark for Relation Extraction (RE), which comprises six distinct text domains and includes multi-label annotations. The dataset includes meta-data collected during annotation, to include explanations and flags of difficult instances.
4 PAPERS • NO BENCHMARKS YET
DiS-ReX is a multilingual dataset for distantly supervised (DS) relation extraction (RE). The dataset has over 1.5 million instances, spanning 4 languages (English, Spanish, German and French). The dataset has 36 positive relation types + 1 no relation (NA) class.
a dataset from A Hierarchical Framework for Relation Extraction with Reinforcement Learning
4 PAPERS • 1 BENCHMARK
Persian dataset for relation extraction, which is an expert-translated version of the "Semeval-2010-Task-8" dataset.
Knowledge about software used in scientific investigations is important for several reasons, for instance, to enable an understanding of provenance and methods involved in data handling. However, software is usually not formally cited, but rather mentioned informally within the scholarly description of the investigation, raising the need for automatic information extraction and disambiguation. Given the lack of reliable ground truth data, we present SoMeSci - Software Mentions in Science - a gold standard knowledge graph of software mentions in scientific articles. It contains high quality annotations (IRR: κ = .82) of 3756 software mentions in 1367 PubMed Central articles. Besides the plain mention of the software, we also provide relation labels for additional information, such as the version, the developer, a URL or citations. Moreover, we distinguish between different types, such as application, plugin or programming environment, as well as different types of mentions, such as usag