named-entity recognition
PulseAugur coverage of named-entity recognition — every cluster mentioning named-entity recognition across labs, papers, and developer communities, ranked by signal.
10 day(s) with sentiment data
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CoffeeBSD seeks help with open-slopware issues on Codeberg
The CoffeeBSD project is seeking assistance with issues related to "open-slopware" on the Codeberg platform. Specifically, help is needed to address problems documented in an issue tracker on Codeberg. The call for help…
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AssemblyAI introduces comprehensive voice AI agent evaluation methods
AssemblyAI has introduced a method for evaluating voice AI agents, focusing on comprehensive testing beyond simple speech-to-text benchmarks. Their approach incorporates simulation-based tests to measure key performance…
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CNM-BERT enhances Chinese NLP by embedding character structure
Researchers have developed CNM-BERT, a novel approach to enhance BERT-based models for Chinese language processing. This method incorporates the compositional structure of Chinese characters, which are often overlooked …
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University of Ottawa wins Latin NER task using Gemini and Claude LLMs
Researchers from the University of Ottawa have achieved top results in the EvaLatin 2026 Named Entity Recognition (NER) shared task for Classical Latin. By employing prompt engineering with large language models Gemini …
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New HomoEnsNER model boosts Gujarati NER performance
Researchers have developed HomoEnsNER, a novel approach to Named Entity Recognition (NER) for the Gujarati language. This method utilizes a homogeneous ensemble of five independently fine-tuned GujaratiBERT models, whic…
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DE-NER framework enhances zero-shot NER using LLM dialogue
Researchers have developed DE-NER, a novel framework for zero-shot Named Entity Recognition (NER) that leverages the conversational capabilities of large language models (LLMs). This approach aims to overcome the limita…
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Graph RAG system tackles knowledge graph update challenges
This article addresses challenges in maintaining corporate knowledge graphs, particularly when dealing with incremental updates. The author, who developed a Graph-RAG system for East Asian corporate intelligence, highli…
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New research explores unsupervised methods for Named Entity Recognition with limited data
This paper investigates unsupervised methods for Named Entity Recognition (NER) when dealing with small or unlabeled datasets across multiple domains. It proposes using unsupervised pre-training to identify entities wit…
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New LA-RL Framework Enhances Large Language Model Information Extraction
Researchers have developed LA-RL, a novel framework designed to improve information extraction capabilities in large language models. This method uses task-specific diagnostic labels to guide self-correction, enabling t…
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New two-step method improves occupation coding accuracy
Researchers have introduced a novel two-step method for occupation coding, a task crucial for labor market research that links job titles to occupational taxonomies. This new approach separates the identification of job…
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AI transforms property data extraction with OCR and NLP
Artificial intelligence is revolutionizing property data extraction by employing technologies like optical character recognition (OCR), natural language processing (NLP), and named-entity recognition (NER). These method…
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CrimeNER Demo launches AI for crime data extraction and classification
Researchers have developed CrimeNER Demo, an AI platform designed for extracting and classifying crime-related information from documents. The platform offers pretrained Named-Entity Recognition (NER) models trained on …
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AI keyword extraction research highlights ethical stewardship for crowdsourced data
A new paper from the University of Oxford explores the use of AI for keyword extraction from crowdsourced collections, using the Their Finest Hour Online Archive as a case study. The research evaluated three Natural Lan…
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Paper details knowledge graph construction for academic conference data
This paper explores the application of deep learning and knowledge graph technology to analyze scientific and technological academic conference data. It details key techniques such as named entity recognition, semantic …
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New paper proposes knowledge graphs for detailed scientific resource portraits
A new paper proposes a method for creating detailed representations of scientific resources by integrating knowledge graph technology, text representation learning, and entity extraction. The authors highlight the explo…
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New CrimeNER Dataset and NER System Released for Law Enforcement
Researchers have developed CrimeNER, a new Named-Entity Recognition (NER) system and database designed to extract critical information from crime-related documents. The CrimeNER-db contains over 1,500 annotated document…
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New framework assesses LLM output certifiability, identifies theoretical limits
Researchers have developed a framework to assess the certifiability of large language model (LLM) outputs for structured generation tasks like named-entity recognition and question answering. They established an impossi…
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New technique uses negative examples to improve LLM information extraction
Researchers have introduced LC-ICL, a novel few-shot technique for information extraction using large language models. This method enhances performance by incorporating both correct (positive) and incorrect (negative) e…
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New BERTomelo model enhances Portuguese NLP tasks
Researchers have developed BERTomelo, a new monolingual encoder model specifically designed for the Portuguese language. This model utilizes the ModernBERT architecture and incorporates optimizations like FlashAttention…
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New multimodal NLP pipeline targets insurance fraud detection
Researchers have developed a multimodal NLP pipeline designed to detect insurance fraud during the First Notice of Loss (FNOL) stage. This framework utilizes synthetic data to generate dialogue transcripts and audio, in…