named-entity recognition
PulseAugur coverage of named-entity recognition — every cluster mentioning named-entity recognition across labs, papers, and developer communities, ranked by signal.
11 day(s) with sentiment data
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LLMs enhance German Central Bank's securities eligibility checks · 3 sources tracked
A new study explores the application of large language models (LLMs) to streamline the German Central Bank's process of verifying securities eligibility. Traditional methods using Named Entity Recognition (NER) face cha…
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AI security: Multi-layered approach to prevent sensitive data leakage
Organizations must implement a multi-layered security strategy to prevent sensitive data from being sent to third-party AI tools. This involves identifying and classifying data such as PII, PHI, secrets, and intellectua…
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New LLM system and dataset enhance product data extraction for Portuguese e-commerce
Researchers have developed AI-PAVE-Br, a system utilizing large language models to improve Product Attribute Value Extraction (PAVE) for Portuguese e-commerce data. This system is designed to handle the complexities and…
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New framework enhances medical named entity recognition for atopic dermatitis
Researchers have developed a novel framework for medical named entity recognition (NER) specifically designed for Chinese clinical texts related to atopic dermatitis. This explanation-guided approach enhances the reliab…
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Many-shot ICL matches BERT performance in NER tasks
A new research paper explores the effectiveness of many-shot in-context learning (ICL) for Named Entity Recognition (NER) using large language models (LLMs). The study found that by scaling ICL to hundreds of examples, …
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Arabic NER model fine-tuned for Egyptian legal documents
This article details the process of fine-tuning a named-entity recognition (NER) model to specifically handle Arabic legal documents from Egypt. The goal is to accurately identify and anonymize sensitive information suc…
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New RedactionBench benchmark reveals LLMs struggle with contextual PII redaction
Researchers have introduced RedactionBench, a new benchmark designed to evaluate how well large language models can redact personally identifiable information (PII) while considering contextual privacy. The benchmark in…
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Local AI cascade achieves high accuracy in de-identifying educational dialogue
Researchers have developed a novel AI cascade framework designed to de-identify sensitive educational dialogue while preserving valuable content. This local system addresses the limitations of commercial LLMs, which req…
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New system AnonShield enables faster, compliant data sharing for CSIRTs
Researchers have developed AnonShield, a system designed for scalable, on-premise pseudonymization of vulnerability data used by computer emergency response teams (CSIRTs). This system utilizes GPU-accelerated named-ent…
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New Corpus Enhances AI Information Extraction for Autoimmunity Research
Researchers have developed AAbAAC, a new annotated corpus specifically for autoimmunity information extraction. This corpus, containing 115 PubMed abstracts, focuses on entities like autoimmune diseases, autoantibodies,…
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PhRAG system automates spare parts pooling with generative AI
A new research paper introduces PhRAG, a hybrid Retrieval-Augmented Generation system designed to improve the pooling of industrial spare parts. The system addresses challenges like inconsistent naming conventions and d…
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LLMs show competitive performance in Named Entity Recognition tasks
A new paper evaluates how well large language models (LLMs) perform on Named Entity Recognition (NER) tasks, moving beyond traditional sequence labeling. The research found that open-source LLMs, when fine-tuned with ef…
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BioConCal boosts LLM biomedical entity recognition accuracy
Researchers have developed BioConCal, a novel scoring system designed to improve the accuracy of biomedical Named Entity Recognition (NER) by LLMs. This system analyzes candidates surfaced by multiple LLMs, moving beyon…
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New LLM Framework LELA Enhances Entity Linking with Zero-Shot Adaptation
Researchers have developed LELA, a new Python library for entity linking that integrates zero-shot Named Entity Recognition (NER). This end-to-end framework aims to be domain-agnostic and practical for real-world NLP ap…
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On-device PII substitution pipeline uses locale-prompting to fix regurgitation
Researchers have developed an on-device pipeline for substituting Personally Identifiable Information (PII) with consistent, type-preserving fake values, aiming to maintain downstream text utility. The system uses a sma…
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New grammar comparison method boosts person name extraction accuracy
Researchers have developed a new method for Named Entity Recognition (NER) specifically for identifying person names. This technique involves comparing concordances from different local grammars to highlight differences…
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Hybrid AI method boosts low-resource Vietnamese NER with LLM data augmentation
Researchers have developed a novel hybrid neurosymbolic framework to improve Named Entity Recognition (NER) for low-resource languages, specifically focusing on Vietnamese. This method combines rule-based processing wit…
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Researchers create Naamah, a large synthetic Sanskrit NER dataset using LLMs
Researchers have developed Naamah, a synthetic dataset of over 100,000 Sanskrit sentences designed to improve Named Entity Recognition (NER) for classical Sanskrit literature. The dataset was generated by combining enti…
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LLMs show promise for recognizing entities in historical texts
Researchers have explored the use of large language models (LLMs) for Named Entity Recognition (NER) in historical texts, a task traditionally requiring extensive annotated data. Utilizing zero-shot and few-shot prompti…
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LLMs improve privacy-utility trade-off for Dutch clinical note de-identification
Researchers have conducted a comparative study on methods for de-identifying Dutch clinical notes to protect patient privacy while allowing for data reuse. The study evaluated traditional methods like differential priva…