Named entity recognition and classification in historical documents: A survey
After decades of massive digitisation, an unprecedented number of historical documents are
available in digital format, along with their machine-readable texts. While this represents a …
available in digital format, along with their machine-readable texts. While this represents a …
A survey of active learning for natural language processing
In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …
language processing (NLP). In addition to a fine-grained categorization of query strategies …
Named entity recognition and classification on historical documents: A survey
After decades of massive digitisation, an unprecedented amount of historical documents is
available in digital format, along with their machine-readable texts. While this represents a …
available in digital format, along with their machine-readable texts. While this represents a …
From zero to hero: Human-in-the-loop entity linking in low resource domains
Entity linking (EL) is concerned with disambiguating entity mentions in a text against
knowledge bases (KB). It is crucial in a considerable number of fields like humanities …
knowledge bases (KB). It is crucial in a considerable number of fields like humanities …
Which examples to annotate for in-context learning? towards effective and efficient selection
Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL). ICL is
efficient as it does not require any parameter updates to the trained LLM, but only few …
efficient as it does not require any parameter updates to the trained LLM, but only few …
Latincy: Synthetic trained pipelines for latin nlp
PJ Burns - arxiv preprint arxiv:2305.04365, 2023 - arxiv.org
This paper introduces LatinCy, a set of trained general purpose Latin-language" core"
pipelines for use with the spaCy natural language processing framework. The models are …
pipelines for use with the spaCy natural language processing framework. The models are …
[HTML][HTML] Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study
S Liu, A Wang, X **u, M Zhong, S Wu - JMIR Medical …, 2024 - medinform.jmir.org
Background: Named entity recognition (NER) models are essential for extracting structured
information from unstructured medical texts by identifying entities such as diseases …
information from unstructured medical texts by identifying entities such as diseases …
On the limitations of simulating active learning
Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects
informative unlabeled data for human annotation, aiming to improve over random sampling …
informative unlabeled data for human annotation, aiming to improve over random sampling …
Scilitllm: How to adapt llms for scientific literature understanding
Scientific literature understanding is crucial for extracting targeted information and garnering
insights, thereby significantly advancing scientific discovery. Despite the remarkable …
insights, thereby significantly advancing scientific discovery. Despite the remarkable …
A deep active learning-based and crowdsourcing-assisted solution for named entity recognition in Chinese historical corpora
C Yan, X Tang, H Yang, J Wang - Aslib Journal of Information …, 2023 - emerald.com
Purpose The majority of existing studies about named entity recognition (NER) concentrate
on the prediction enhancement of deep neural network (DNN)-based models themselves …
on the prediction enhancement of deep neural network (DNN)-based models themselves …