Nested Named Entity Recognition
44 papers with code • 6 benchmarks • 11 datasets
Nested named entity recognition is a subtask of information extraction that seeks to locate and classify nested named entities (i.e., hierarchically structured entities) mentioned in unstructured text (Source: Adapted from Wikipedia).
Datasets
Most implemented papers
Merge and Label: A novel neural network architecture for nested NER
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
Neural Architectures for Nested NER through Linearization
We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label.
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019
Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0. 3738.
A Boundary-aware Neural Model for Nested Named Entity Recognition
We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels.
Bipartite Flat-Graph Network for Nested Named Entity Recognition
In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers.
Named Entity Recognition as Dependency Parsing
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities.
A Boundary Regression Model for Nested Named Entity Recognition
Then, a regression operation is introduced to regress boundaries of NEs in a sentence.
Nested Named Entity Recognition with Partially-Observed TreeCRFs
With the TreeCRF we achieve a uniform way to jointly model the observed and the latent nodes.
Structured Prediction as Translation between Augmented Natural Languages
We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
We present a joint model for entity-level relation extraction from documents.