# named-entity-recognition Published articles for named-entity-recognition. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## CardioLM: A multilingual suite of small language models for the cardiology domain DevFeed: [CardioLM: A multilingual suite of small language models for the cardiology domain]() Original publisher: [Read original article]() Author: Manuela-Daniela Danu, Miguel Rodr\\'iguez-Ortega, Alberto Becerra-Tome, Pablo Arancibia, Fernando Gallego-Donoso, Lorenzo Pratesi, Xi Bai, Andrei-Damian Dinescu, Salvador Lima-L\\'opez, Andrea Guala, Sa Published: 2026-11-06T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [ner](), [Fine-tuning](), [machine-translation](), [Large Language Model]() Tags: [annotations](), [benchmark](), [biomedical](), [cs-ai](), [cs-cl](), [deployment](), [language-models](), [languages](), [machine-translation](), [named-entity-recognition](), [natural-language-processing](), [pretraining]() ### AI overview CardioCCC contains 508 Spanish cardiology case reports extended to Czech, Dutch, English, Italian, Romanian, and Swedish, with clinician-reviewed annotations for diseases, symptoms, procedures, and medications. CardioLM pairs language-specific CardioBERTa encoders, continued-pretrained on cardiology and biomedical text, with CardioNER models fine-tuned for entity recognition. Specialty pretraining lowered pseudo-perplexity by up to eightfold but improved strict span F1 by no more than 0.04, mainly where base models lacked biomedical coverage. Jointly trained multilingual XLM-RoBERTa-large models performed best in nearly every language and entity type, beating the strongest monolingual system by up to 0.13 F1. Relaxed boundary matching recovered more F1 for diseases, procedures, and symptoms than medications. Clinicians rated CardioNER higher than a rule-and-dictionary baseline (73% versus 26% mean rating). ### Source excerpt arXiv:2611.07940v1 Announce Type: cross Abstract: Background: Cardiovascular care generates large volumes of clinical free-text documentation in local languages. However, clinical natural language processing (NLP) resources remain concentrated in English, and no existing benchmark is cardiology-specific, multilingual, and broad in entity coverage. Methods: We present two contributions. The first is CardioCCC, a corpus of 508 cardiology case reports originally published in Spanish and extended to Czech, Dutch, English, Italian, Romanian, and Swedish through machine translation, annotation projection, and clinician review, with annotations covering diseases, symptoms, procedures, and medications. The second is CardioLM, comprising CardioBERTa, a set of language-specific encoders obtained by continued pretraining on cardiology and biomedical corpora, and CardioNER, a set of named entity recognition (NER) models fine-tuned on CardioCCC. Results: Cardiology-specific pretraining reduced pseudo-perplexity by up to a factor of eight, yet changed strict span-level F1 by at most 0.04, with benefits concentrated in languages whose backbones lacked biomedical coverage. The jointly trained multilingual XLM-RoBERTa-large models achieved the best strict F1 in nearly every language and entity type, exceeding the strongest monolingual system by up to 0.13 F1. A large share of residual errors concerned boundary detection: relaxed matching recovered 0.10-0.23 F1 for disease, procedure, and symptom, but only 0.01-0.07 for medication. Clinicians rated CardioNER well above a rule- and dictionary-based baseline, with the mean rating across entity types rising from 26% to 73%. Conclusions: Specialty-specific pretraining is not the decisive factor for downstream entity recognition, whereas multilingual joint training yields the strongest results in nearly all settings and a simpler deployment path for multi-country clinical information extraction. ## CrimeNER: Curated Dataset and Methods for Research and Investigation in Language Intelligence DevFeed: [CrimeNER: Curated Dataset and Methods for Research and Investigation in Language Intelligence]() Original publisher: [Read original article]() Author: Miguel Lopez-Duran, Julian Fierrez, Aythami Morales, Ruben Tolosana, Daniel DeAlcala, Gonzalo Mancera, Javier Irigoyen, Oscar Delgado, Francisco Jurado, Alvaro Ortigosa Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Databases]() Topics: [ner](), [managing large-scale text data in AI](), [AI Chat]() Tags: [attacks](), [crime](), [cs-ai](), [cs-cl](), [cs-db](), [database](), [dataset](), [department-of-justice](), [experiments](), [few-shot](), [flat](), [github](), [language-models](), [law-enforcement](), [named-entity-recognition](), [nested]() ### AI overview CrimeNER-db addresses the shortage of annotated data for extracting entities from crime-related documents. It contains more than 1,500 annotated documents drawn from public reports of terrorist attacks and U.S. Department of Justice press notes, organized into four coarse and 21 fine-grained entity types. The authors assess the database using supervised flat and nested NER architectures, plus zero-shot and few-shot experiments with large language models. The abstract does not report comparative results. ### Source excerpt arXiv:2603.02150v3 Announce Type: replace-cross Abstract: The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. The extraction of this information can be interpreted as a Named-Entity Recognition (NER) task. However, there is a considerable lack of adequately annotated data on general real-world crime scenarios. To address this issue, we present CrimeNER, a case study of crime-related NER, and a general crime-related Named-Entity Recognition database (CrimeNER-db), consisting of more than 1.5K annotated documents extracted from public reports of terrorist attacks and the US Department of Justice's press notes. We define 4 coarse types of crime entity and 21 fine-grained entity types. We address the quality of the presented database with fully supervised experiments using flat and nested NER architectures and zero- and few-shot experiments using Large Language Models (LLMs). The database is available on GitHub. ## Understanding GLiFormer's Benchmarks and Schema-Driven Extraction DevFeed: [Understanding GLiFormer's Benchmarks and Schema-Driven Extraction]() Original publisher: [Read original article]() Author: aimodels44 Published: 2026-09-22T20:50:51Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Benchmark](), [ner](), [text-classification](), [Structured-data](), [Embeddings]() Tags: [ai-engineering](), [benchmarks](), [classification](), [datasets](), [deep-learning](), [evaluation](), [gliformer](), [knowledgator](), [model](), [named-entity-recognition](), [pydantic-validation](), [python](), [schema-driven-extraction](), [structured](), [structured-record-extraction]() ### AI overview An overview of GLiFormer Large v1, a 575.6-million-parameter Apache-2.0 model for schema-driven information extraction and text representation. The article examines its entity extraction, classification, relation extraction, structured-record, and embedding capabilities, with Python examples and benchmark results showing substantial variation by task and dataset. ### Source excerpt Explore GLiFormer Large v1's extraction tasks, reported benchmarks, Python examples, and limitations across entities, relations, and structured records.