Papers with Entity mentions

2 papers
A Neural Layered Model for Nested Named Entity Recognition (N18-1)

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Challenge: Entity mentions embedded in longer entity mentions are referred to as nested entities due to the properties of natural language.
Approach: They propose a neural model that dynamically stacks flat NER layers to identify nested entities by capturing sequential context representation with bidirectional long-term memory.
Outcome: The proposed model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively in terms of F-score.
Multi-Cell Compositional LSTM for NER Domain Adaptation (2020.acl-main)

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Challenge: Named entity recognition (NER) is a challenging but practical problem.
Approach: They propose a multi-cell compositional LSTM structure for multi-task learning . they model each entity type using a separate cell state .
Outcome: Empirical results show that the proposed method outperforms multi-task learning methods and achieves the best results.

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