Joint Entity and Relation Extraction

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: - Over 1.8 million articles (excluding wire services articles that appeared during the covered period). - Over 650,000 article summaries written by library scientists. - Over 1,500,000 articles manually tagged by library scientists with tags drawn from a normalized indexing vocabulary of people, organizations, locations and topic descriptors. - Over 275,000 algorithmically-tagged articles that have been hand verified by the online production staff at nytimes.com. As part of the New York Times' indexing procedures, most articles are manually summarized and tagged by a staff of library scientists. This collection contains over 650,000 article-summary pairs which may prove to be useful in the development and evaluation of algorithms for automated document summarization. Also, over 1.5 million documents have at least one tag. Articles are tagged for persons, places, organizations, titles and topics using a controlled vocabulary that is applied consistently across articles. For instance if one article mentions "Bill Clinton" and another refers to "President William Jefferson Clinton", both articles will be tagged with "CLINTON, BILL". Source: https://catalog.ldc.upenn.edu/LDC2008T19

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.

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. DocRED contains 132,375 entities and 56,354 relational facts annotated on 5,053 Wikipedia documents. In addition to the human-annotated data, the dataset provides large-scale distantly supervised data over 101,873 documents. Source: DocRED: A Large-Scale Document-Level Relation Extraction Dataset Image Source: DocRED: A Large-Scale Document-Level Relation Extraction Dataset

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. Initially, the dataset was used for the WebNLG natural language generation challenge which consists of mapping the sets of triplets to text, including referring expression generation, aggregation, lexicalization, surface realization, and sentence segmentation. The corpus is also used for a reverse task of triplets extraction. Versioning history of the dataset can be found here. Source: Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation Image Source: https://paperswithcode.com/paper/creating-training-corpora-for-nlg-micro/ It's also available here: https://huggingface.co/datasets/web_nlg Note: "The v3 release (release_v3.0_en, release_v3.0_ru) for the WebNLG2020 challenge also supports a semantic parsing task."

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. Source: http://nlp.cs.washington.edu/sciIE/ Image Source: http://nlp.cs.washington.edu/sciIE/

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. We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14,579 entities and 10,889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets. We also release an inference dataset, which contains automatically generated annotations for 220,763 MIMIC-CXR reports (around 6 million entities and 4 million relations) and 500 CheXpert reports (13,783 entities and 9,908 relations) with mappings to associated chest radiographs.

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. Source: https://catalog.ldc.upenn.edu/LDC2006T06 Image Source: https://arxiv.org/pdf/1811.06031.pdf

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.

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).

The Dataset is part of the KELM corpus This is the Wikipedia text--Wikidata KG aligned corpus used to train the data-to-text generation model. Please note that this is a corpus generated with distant supervision and should not be used as gold standard for evaluation. It consists of 3 files: https://storage.googleapis.com/gresearch/kelm-corpus/updated-2021/quadruples-train.tsv https://storage.googleapis.com/gresearch/kelm-corpus/updated-2021/quadruples-validation.tsv https://storage.googleapis.com/gresearch/kelm-corpus/updated-2021/quadruples-test.tsv Each file contains one example per line. Each example is a json object with three fields: triples: A list of triples of the form (subject, relation, object). eg. (Person X, award received, Award Y). If the triple has a subproperty, then it is quadruple instead. eg. (Person X, Award Y, received on, Date Z). serialized triples: triples concatenated together as used for input to T5. The format is "<subject> <relation> <object>" where some subjects have multiple relations, e.g. "<subject> <relation1> <object1> <relation2> <object2> <relation3> <object3>". For more details on how these relations are grouped, please refer to the paper. sentence: The wikipedia sentence aligned to these triples. The names, aliases and Wikidata Ids of the entities can be found in https://storage.googleapis.com/gresearch/kelm-corpus/updated-2021/entities.jsonl.

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.

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.

Symlink is a SemEval shared task of extracting mathematical symbols and their descriptions from LaTeX source of scientific documents. This is a new task in SemEval 2022, which attracted 180 individual registrations and 59 final submissions from 7 participant teams.

We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents (the named entity recognition part) and link them to their numerical values (the relation extraction part). Challenges include the fuzzy borders of entities and finding the correct numeric value/year pair for each entity.

A curated evaluation dataset for end-to-end Relation Extraction of relationships between organisms and natural-products. Details about the manual annotation: * For Chemicals: - The chemical labels are annotated as they appear in the abstract. - In abstracts, singular chemicals and classes of chemicals produced by a specific organism were distinguished. - The "type" attribute {“chemical”, “class”} is used to indicate the nature of the mentioned name. - A "class" attribute for chemical entities has also been included if class information is present in the abstract. - A Wikidata and PubChem identifiers were assigned to chemicals and classes when available. * For Organisms: - The organism labels are annotated as they appear in the abstract. - If in an abstract, the genus name was mention first, e.g. "Plakinastrella sp." and then the specie name e.g "Plakinastrella clathrata" is precise, then only the specie name is used. - A Wikidata identifier was assigned to all organisms. - In some abstracts, only the genus name is mentioned. * For Relations: - Only the relations explicitly mentioned in the abstract are reported in the output labels. - Relations are reported in their order of appearance in the abstract.

The DocRED Information Extraction (DocRED-IE) dataset extends the DocRED dataset for the Document-level Closed Information Extraction (DocIE) task. DocRED-IE is a multi-task dataset and allows for 5 subtasks: (i) Document-level Relation Extraction, (ii) Mention Detection, (iii) Entity Typing, (iv) Entity Disambiguation, (v) Coreference Resolution, as well as combinations thereof such as Named Entity Recognition (NER) or Entity Linking. The DocRED-IE dataset also allows for the end-to-end tasks of: (i) DocIE and (ii) Joint Entity and Relation Extraction. DocRED-IE comprises sentence-level and document-level facts, thereby describing short as well as long-range interactions within an entire document.