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Datasets for large language models: A comprehensive survey
This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …
A survey on recent approaches for natural language processing in low-resource scenarios
Deep neural networks and huge language models are becoming omnipresent in natural
language applications. As they are known for requiring large amounts of training data, there …
language applications. As they are known for requiring large amounts of training data, there …
[HTML][HTML] A survey on named entity recognition—datasets, tools, and methodologies
Natural language processing (NLP) is crucial in the current processing of data because it
takes into account many sources, formats, and purposes of data as well as information from …
takes into account many sources, formats, and purposes of data as well as information from …
Leverage lexical knowledge for Chinese named entity recognition via collaborative graph network
The lack of word boundaries information has been seen as one of the main obstacles to
develop a high performance Chinese named entity recognition (NER) system. Fortunately …
develop a high performance Chinese named entity recognition (NER) system. Fortunately …
A review on method entities in the academic literature: Extraction, evaluation, and application
In scientific research, the method is an indispensable means to solve scientific problems and
a critical research object. With the advancement of sciences, many scientific methods are …
a critical research object. With the advancement of sciences, many scientific methods are …
Noisy-labeled NER with confidence estimation
Recent studies in deep learning have shown significant progress in named entity
recognition (NER). Most existing works assume clean data annotation, yet a fundamental …
recognition (NER). Most existing works assume clean data annotation, yet a fundamental …
Empirical analysis of unlabeled entity problem in named entity recognition
In many scenarios, named entity recognition (NER) models severely suffer from unlabeled
entity problem, where the entities of a sentence may not be fully annotated. Through …
entity problem, where the entities of a sentence may not be fully annotated. Through …
A pre-training and self-training approach for biomedical named entity recognition
Named entity recognition (NER) is a key component of many scientific literature mining
tasks, such as information retrieval, information extraction, and question answering; …
tasks, such as information retrieval, information extraction, and question answering; …
Misrobærta: transformers versus misinformation
Misinformation is considered a threat to our democratic values and principles. The spread of
such content on social media polarizes society and undermines public discourse by …
such content on social media polarizes society and undermines public discourse by …
Ecomgpt-ct: Continual pre-training of e-commerce large language models with semi-structured data
Large Language Models (LLMs) pre-trained on massive corpora have exhibited remarkable
performance on various NLP tasks. However, applying these models to specific domains still …
performance on various NLP tasks. However, applying these models to specific domains still …