Better feature integration for named entity recognition
It has been shown that named entity recognition (NER) could benefit from incorporating the
long-distance structured information captured by dependency trees. We believe this is …
long-distance structured information captured by dependency trees. We believe this is …
[PDF][PDF] Investigating the impact of syntax-enriched transformers on quantity extraction in scientific texts
Measurement extraction is an information extraction subtask focused on extracting quantities
and their dependent entities within a given scientific text. Quantity extraction is the first and …
and their dependent entities within a given scientific text. Quantity extraction is the first and …
Attention and edge-label guided graph convolutional networks for named entity recognition
R Zhou, Z **e, J Wan, J Zhang, Y Liao… - Proceedings of the 2022 …, 2022 - aclanthology.org
It has been shown that named entity recognition (NER) could benefit from incorporating the
long-distance structured information captured by dependency trees. However, dependency …
long-distance structured information captured by dependency trees. However, dependency …
A method for cultural relics named entity recognition based on enhanced lexical features
Y Li, H Yan, Y Yang, X Wang - 2024 International Joint …, 2024 - ieeexplore.ieee.org
Named entity recognition (NER) of cultural relic data faces challenges in identifying entity
boundaries and types due to the unique nature of word formation for these entities. To …
boundaries and types due to the unique nature of word formation for these entities. To …
Enhanced named entity recognition through joint dependency parsing
Named entity recognition (NER) is the task of identifying and classifying named entities from
texts. NER can benefit from linguistic dependency information, yet existing NER models can …
texts. NER can benefit from linguistic dependency information, yet existing NER models can …
Linguistic dependency guided graph convolutional networks for named entity recognition
X Sun, J Zhou, S Wang, X Li, B Zheng, D Liu - International Conference on …, 2022 - Springer
The GCN model used for named entity recognition (NER) tasks reflects promising results by
capturing the long-distance syntactic dependency between words in sentences. However …
capturing the long-distance syntactic dependency between words in sentences. However …
Structure-based Models for Neural Information Extraction
APB Veyseh - 2023 - search.proquest.com
Abstract Information Extraction (IE) is one of the important fields in Natural Language
Processing. IE models can be exploited to obtain meaningful information from raw text and …
Processing. IE models can be exploited to obtain meaningful information from raw text and …
[PDF][PDF] Structure-based Models for Neural Information Extraction
A Pouran Ben Veyseh - 2024 - scholarsbank.uoregon.edu
Information Extraction (IE) is one of the important fields in Natural Language Processing. IE
models can be exploited to obtain meaningful information from raw text and provide them in …
models can be exploited to obtain meaningful information from raw text and provide them in …
BGGF: a gated information fusion model for biomedical entity recognition
L Li, F Zhang - 2021 IEEE International Conference on …, 2021 - ieeexplore.ieee.org
Extracting valuable information from the biomedical literature is gaining attention among
researchers, and Biomedical Named Entity Recognition (BioNER) becomes one of the most …
researchers, and Biomedical Named Entity Recognition (BioNER) becomes one of the most …
Neural Sequence Labeling on Social Media Text
G Aguilar - 2020 - uh-ir.tdl.org
As social media (SM) brings opportunities to study societies across the world, it also brings a
variety of challenges to automate the processing of SM language. In particular, most of the …
variety of challenges to automate the processing of SM language. In particular, most of the …