.png){kind=icon}
Return
Hypergraph network model for nested entity mention recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2020.09.077.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose a hypergraph network (HGN) model to recognize the nested entity mentions in texts. This model can learn the representations for the sequence structures of natural languages and the representations for the hypergraph structures of nested entity mentions. Mainstream methods recognize an entity mention by separately tagging the words or the gaps between words, which may complicate the problem and not be favorable for capturing the overall features of the mention. To solve these issues, the HGN model treats each entity mention as a whole and tags it with one label. We represent each sentence as a hypergraph, in which nodes represent words and hyperedges represent entity mentions. Thus, entity mention recognition (EMR) is transformed into a task of classifying the hyperedges. The HGN model firstly uses encoders to extract the features and learn a hypergraph representation, and then recognizes entity mentions by tagging every hyperedge. The experiments on three standard datasets demonstrate our model outperforms the previous models for nested EMR. We openly release the source code at https://github.com/nlplab-ie/HGN. (c) 2020 Elsevier B.V. All rights reserved.
Keywords:
Named entity recognition
Information extraction
Neural networks
Natural language processing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
No organization information available
Cited Papers
ShareSaveMutational landscape of patients with acute myeloid leukemia or myelodysplastic syndromes in the context of RUNX1 mutation.png)
Hematology
OAAI
HematologyIF0
OAAIShareSaveShareSaveShareSave
View more