Nested named entity recognition as latent lexicalized constituency parsing
Nested named entity recognition (NER) has been receiving increasing attention.
Recently,(Fu et al, 2021) adapt a span-based constituency parser to tackle nested NER …
Recently,(Fu et al, 2021) adapt a span-based constituency parser to tackle nested NER …
Crossroads, buildings and neighborhoods: A dataset for fine-grained location recognition
Abstract General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly
annotate coarse-grained location entities such as a country or a city. But many applications …
annotate coarse-grained location entities such as a country or a city. But many applications …
Coarse-to-fine knowledge graph domain adaptation based on distantly-supervised iterative training
Modern supervised learning neural network models require a large amount of manually
labeled data, which makes the construction of domain-specific knowledge graphs time …
labeled data, which makes the construction of domain-specific knowledge graphs time …
Exploring the effects of drug, disease, and protein dependencies on biomedical named entity recognition: A comparative analysis
Background: Biomedical named entity recognition is one of the important tasks of biomedical
literature mining. With the development of natural language processing technology, many …
literature mining. With the development of natural language processing technology, many …
DualFLAT: Dual Flat-Lattice Transformer for domain-specific Chinese named entity recognition
Y **ao, Z Ji, J Li, Q Zhu - Information Processing & Management, 2025 - Elsevier
Recently, lexicon-enhanced methods for Chinese Named Entity Recognition (NER) have
achieved great success which requires a high-quality lexicon. However, for the domain …
achieved great success which requires a high-quality lexicon. However, for the domain …
BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework
X Zheng, H Du, X Luo, F Tong, W Song, D Zhao - BMC bioinformatics, 2022 - Springer
Background Automatic and accurate recognition of various biomedical named entities from
literature is an important task of biomedical text mining, which is the foundation of extracting …
literature is an important task of biomedical text mining, which is the foundation of extracting …
Improving Named Entity Recognition via Bridge-based Domain Adaptation
Recent studies have shown remarkable success in cross-domain named entity recognition
(cross-domain NER). Despite the promising results, existing methods mainly utilize pre …
(cross-domain NER). Despite the promising results, existing methods mainly utilize pre …
Uncertainty Quantification for In-Context Learning of Large Language Models
In-context learning has emerged as a groundbreaking ability of Large Language Models
(LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in …
(LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in …
Gradient Rewiring for Editable Graph Neural Network Training
Deep neural networks are ubiquitously adopted in many applications, such as computer
vision, natural language processing, and graph analytics. However, well-trained neural …
vision, natural language processing, and graph analytics. However, well-trained neural …
Automatic information extraction in the AI chip domain using gated interactive attention and probability matrix encoding method
X Jiang, K He, Y Chen - Expert Systems with Applications, 2023 - Elsevier
Artificial intelligence (AI) that utilizes neural networks (NNs) has a broad range of
applications. However, NNs necessitate significant amounts of computation and data …
applications. However, NNs necessitate significant amounts of computation and data …