Nested Named Entity Recognition

The GENIA corpus is the primary collection of biomedical literature compiled and annotated within the scope of the GENIA project. The corpus was created to support the development and evaluation of information extraction and text mining systems for the domain of molecular biology. The corpus contains 1,999 Medline abstracts, selected using a PubMed query for the three MeSH terms “human”, “blood cells”, and “transcription factors”. The corpus has been annotated with various levels of linguistic and semantic information. The primary categories of annotation in the GENIA corpus and the corresponding subcorpora are: * Part-of-Speech annotation * Constituency (phrase structure) syntactic annotation * Term annotation * Event annotation * Relation annotation * Coreference annotation Source: http://www.geniaproject.org/genia-corpus Image Source: http://www.geniaproject.org/genia-corpus

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

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. Source: https://catalog.ldc.upenn.edu/LDC2005T09

NNE is a dataset for Nested Named Entity Recognition in English Newswire

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

AMALGUM is a machine annotated multilayer corpus following the same design and annotation layers as GUM, but substantially larger (around 4M tokens). The goal of this corpus is to close the gap between high quality, richly annotated, but small datasets, and the larger but shallowly annotated corpora that are often scraped from the Web.

DaN+ is a new multi-domain corpus and annotation guidelines for Danish nested named entities (NEs) and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language.

The Chilean Waiting List corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 10,000 referrals (including medical and dental notes) was manually annotated with ten entity types with clinical relevance, keeping 1,000 annotations for a future shared task. A trained medical doctor or dentist annotated these referrals and then, together with three other researchers, consolidated each of the annotations. The annotated corpus has more than 48% of entities embedded in other entities or containing another. This corpus can be a useful resource to build new models for Nested Named Entity Recognition (NER). This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. Hugging Face datasets: https://huggingface.co/plncmm. After predicting over each entity type, merge the prediction to obtain your final micro f1-score. This is for a fair comparison with actual state-of-the-art models.

LegalNERo is a manually annotated corpus for named entity recognition in the Romanian legal domain. It provides gold annotations for organizations, locations, persons, time and legal resources mentioned in legal documents. Additionally it offers GEONAMES codes for the named entities annotated as location (where a link could be established). The LegalNERo corpus is available in different formats: span-based, token-based and RDF. The Linguistic Linked Open Data (LLOD) version is provided in RDF-Turtle format.

Named Entity (NER) annotations of the Hebrew Treebank (Haaretz newspaper) corpus, including: morpheme and token level NER labels, nested mentions, and more. We publish the NEMO corpus in the TACL paper "Neural Modeling for Named Entities and Morphology (NEMO^2)" [1], where we use it in extensive experiments and analyses, showing the importance of morphological boundaries for neural modeling of NER in morphologically rich languages. Code for these models and experiments can be found in the NEMO code repo. ## Main features: 1. Morpheme, token-single and token-multi sequence labels. Morpheme labels provide exact boundaries, token-multi provide partial sub-word morphological but no exact boundaries, token-single provides only token-level information. 1. All annotations are in BIOSE format (B=Begin, I=Inside, O=Outside, S=Singleton, E=End). 1. Widely-used OntoNotes entity category set: GPE (geo-political entity), PER (person), LOC (location), ORG (organization), FAC (facility), EVE (event), WOA (work-of-art), ANG (language), DUC (product). 1. NEMO includes NER annotations for the two major versions of the Hebrew Treebank, UD (Universal Dependency) and SPMRL. These can be aligned to the other morphosyntactic information layers of the treebank using bclm 1. We provide nested mentions. Only the first, widest, layer is used in the NEMO^2 paper. We invite you to take on this challenge! 1. Guidelines used for annotation are provided here. 1. Corpus was annotated by two native Hebrew speakers of academic education, and curated by the project manager. We provide the original annotations made by the annotators as well to promote work on learning with disagreements. 1. Annotation was performed using WebAnno (version 3.4.5) ## Basic Corpus Statistics | | train | dev | test | |------------------------------| --:| --:| --:| | Sentences | 4,937 | 500 | 706 | | Tokens | 93,504 | 8,531 | 12,619 | | Morphemes | 127,031 | 11,301 | 16,828 | | All mentions | 6,282 | 499 | 932 | | Type: Person (PER) | 2,128 | 193 | 267 | | Type: Organization (ORG) | 2,043 | 119 | 408 | | Type: Geo-Political (GPE) | 1,377 | 121 | 195 | | Type: Location (LOC) | 331 | 28 | 41 | | Type: Facility (FAC) | 163 | 12 | 11 | | Type: Work-of-Art (WOA) | 114 | 9 | 6 | | Type: Event (EVE) | 57 | 12 | 0 | | Type: Product (DUC) | 36 | 2 | 3 | | Type: Language (ANG) | 33 | 3 | 1 | ## Evaluation An evaluation script is provided in the NEMO code repo along with evaluation instructions. ## Citation @article{10.1162/tacl_a_00404, author = {Bareket, Dan and Tsarfaty, Reut}, title = "{Neural Modeling for Named Entities and Morphology (NEMO2)}", journal = {Transactions of the Association for Computational Linguistics}, volume = {9}, pages = {909-928}, year = {2021}, month = {09}, abstract = "{Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically rich languages (MRLs) pose a challenge to this basic formulation, as the boundaries of named entities do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings (i.e., where no gold morphology is available). We empirically investigate these questions on a novel NER benchmark, with parallel token- level and morpheme-level NER annotations, which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture, in which NER precedes and prunes morphological decomposition, greatly outperforms the standard pipeline, where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks.}", issn = {2307-387X}, doi = {10.1162/tacl_a_00404}, url = {https://doi.org/10.1162/tacl\_a\_00404}, eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00404/1962472/tacl\_a\_00404.pdf}, }

A vast amount of information in the biomedical domain is available as natural language free text. An increasing number of documents in the field are written in languages other than English. Therefore, it is essential to develop resources, methods and tools that address Natural Language Processing in the variety of languages used by the biomedical community. In this paper, we report on the development of an extensive corpus of biomedical documents in French annotated at the entity and concept level. Three text genres are covered, comprising a total of 103,056 words. Ten entity categories corresponding to UMLS Semantic Groups were annotated, using automatic pre-annotations validated by trained human annotators. The pre-annotation method was found helful for entities and achieved above 0.83 precision for all text genres. Overall, a total of 26,409 entity annotations were mapped to 5,797 unique UMLS concepts.

CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems. First track is devoted to Nested term extraction (in sequence labeling format) (also called Nested Term Identification).

CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems. Second track is devoted to Nested term extraction (in sequence labeling format) and classification (labels are specific, common, nomen).

CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems. Third track is devoted to Nested term extraction (in sequence labeling format) and classification (labels are specific, common, nomen) in cross-domain task.