Entity Typing is an important task in text analysis. Assigning types (e.g., person, location, organization) to mentions of entities in documents enables effective structured analysis of unstructured text corpora. The extracted type information can be used in a wide range of ways (e.g., serving as primitives for information extraction and knowledge base (KB) completion, and assisting question answering). Traditional Entity Typing systems focus on a small set of coarse types (typically fewer than 10). Recent studies work on a much larger set of fine-grained types which form a tree-structured hierarchy (e.g., actor as a subtype of artist, and artist is a subtype of person).
OntoNotes 5.0 is a large corpus comprising various genres of text (news, conversational telephone speech, weblogs, usenet newsgroups, broadcast, talk shows) in three languages (English, Chinese, and Arabic) with structural information (syntax and predicate argument structure) and shallow semantics (word sense linked to an ontology and coreference). OntoNotes Release 5.0 contains the content of earlier releases - and adds source data from and/or additional annotations for, newswire, broadcast news, broadcast conversation, telephone conversation and web data in English and Chinese and newswire data in Arabic. Source: https://catalog.ldc.upenn.edu/LDC2013T19
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.
The FIGER dataset is an entity recognition dataset where entities are labelled using fine-grained system 112 tags, such as person/doctor, art/written_work and building/hotel. The tags are derivied from Freebase types. The training set consists of Wikipedia articles automatically annotated with distant supervision approach that utilizes the information encoded in anchor links. The test set was annotated manually. Source: http://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/5152
Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)).
AIDA CoNLL-YAGO contains assignments of entities to the mentions of named entities annotated for the original CoNLL 2003 entity recognition task. The entities are identified by YAGO2 entity name, by Wikipedia URL, or by Freebase mid. Source: AIDA CoNLL-YAGO
The Open Entity dataset is a collection of about 6,000 sentences with fine-grained entity types annotations. The entity types are free-form noun phrases that describe appropriate types for the role the target entity plays in the sentence. Sentences were sampled from Gigaword, OntoNotes and web articles. On average each sentence has 5 labels. Source: Ultra-Fine Entity Typing Image Source: Ultra-Fine Entity Typing
A large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. Source: PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution
GUM is an open source multilayer English corpus of richly annotated texts from twelve text types. Annotations include: * Multiple POS tags, morphological features and lemmatization * Sentence segmentation and rough speech act * Document structure in TEI XML (paragraphs, headings, figures, etc.) * ISO date/time annotations * Speaker and addressee information (where relevant) * Constituent and dependency syntax * Information status (given, accessible, new, split antecedent) * Entity and coreference annotation, including bridging anaphora * Entity linking (Wikification) * Discourse parses in Rhetorical Structure Theory and discourse dependencies
A dataset for fine-grained entity typing of knowledge graph entities built from Freebase. It can be used to evaluate entity representations and also mention-level entity typing.
WikiSRS is a novel dataset of similarity and relatedness judgments of paired Wikipedia entities (people, places, and organizations), as assigned by Amazon Mechanical Turk workers. Source: https://github.com/OSU-slatelab/WikiSRS
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.