NER
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Benchmarks
Most implemented
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
CrossNER: Evaluating Cross-Domain Named Entity Recognition
AraBERT: Transformer-based Model for Arabic Language Understanding
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
RITA: Automatic Framework for Designing of Resilient IoT Applications
RaTEScore: A Metric for Radiology Report Generation
Papers
Flippi: End To End GenAI Assistant for E-Commerce
The emergence of conversational assistants has fundamentally reshaped user interactions with digital platforms. This paper introduces Flippi-a cutting-edge, end-to-end conversational assistant powered by large language m…
Intent Detectionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models
Supervised fine-tuning (SFT) is widely used to align large language models (LLMs) with information extraction (IE) tasks, such as named entity recognition (NER). However, annotating such fine-grained labels and training …
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERImproving Named Entity Transcription with Contextual LLM-based Revision
With recent advances in modeling and the increasing amount of supervised training data, automatic speech recognition (ASR) systems have achieved remarkable performance on general speech. However, the word error rate (WER…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Language ModelingLanguage Modelling+4Better Semi-supervised Learning for Multi-domain ASR Through Incremental Retraining and Data Filtering
Fine-tuning pretrained ASR models for specific domains is challenging when labeled data is scarce. But unlabeled audio and labeled data from related domains are often available. We propose an incremental semi-supervised …
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NEREfficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering
Fine-tuning pretrained ASR models for specific domains is challenging for small organizations with limited labeled data and computational resources. Here, we explore different data selection pipelines and propose a robus…
DecoderDomain Adaptationnamed-entity-recognitionNamed Entity Recognition+2EL4NER: Ensemble Learning for Named Entity Recognition via Multiple Small-Parameter Large Language Models
In-Context Learning (ICL) technique based on Large Language Models (LLMs) has gained prominence in Named Entity Recognition (NER) tasks for its lower computing resource consumption, less manual labeling overhead, and str…
Ensemble LearningIn-Context Learningnamed-entity-recognitionNamed Entity Recognition+3