| Challenge: | Existing methods to recognize nested mentions are based on Stack-LSTM . nesting mentions can be used for downstream tasks like question answering and relation extraction. |
| Approach: | They propose a scalable transition-based method to model the nested structure of mentions. |
| Outcome: | The proposed method gets the state-of-the-art performance in ACE datasets showing its effectiveness in detecting nested mentions. |
Similar 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. |
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks (P19-1)
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| Challenge: | Named entity recognition (NER) approaches restrict each word belonging to at most one entity mention. |
| Approach: | They propose to model and leverage the head-driven phrase structures of entity mentions to solve this problem. |
| Outcome: | The proposed architecture achieves state-of-the-art on three standard nested entity mention detection benchmarks. |
Neural Mention Detection (2020.lrec-1)
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| Challenge: | Mention detection is an important preprocessing step for downstream applications such as NER and coreference resolution. |
| Approach: | They propose and compare three approaches to mention detection using ELMO embeddings and a biaffine classifier. |
| Outcome: | The proposed model outperforms state-of-the-art models on the GENIA corpora and improves on mention recall. |
Nested Named Entity Recognition Revisited (N18-1)
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| Challenge: | Existing methods for named entity recognition and mention detection ignore nested entities along with any semantic relations between them. |
| Approach: | They propose a recurrent neural network-based approach to handle named entity recognition and nested entity mention detection simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three standard datasets. |
An Effective Transition-based Model for Discontinuous NER (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) data sets often contain mentions consisting of discontinuous spans. |
| Approach: | They propose a transition-based model with generic neural encoding for discontinuous NER that can recognize discontinuous mentions without sacrificing the accuracy on continuous mentions. |
| Outcome: | The proposed model can recognize discontinuous mentions without sacrificing accuracy on continuous mentions. |
Recognizing Complex Entity Mentions: A Review and Future Directions (P18-3)
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| Challenge: | Named entity recognition (NER) is a task of identifying and classifying named entities (NE) within text. |
| Approach: | They review existing methods for identifying and classifying named entities within text . they identify the research gap and propose a new approach to tackle these problems . |
| Outcome: | The proposed methods address the identified identified gaps in the literature and provide recommendations for future work. |
Neural Architectures for Nested NER through Linearization (P19-1)
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| Challenge: | a nested named entity recognition (NER) is a set of entities that can overlap and be labeled with more than one label. |
| Approach: | They propose two neural network architectures for nested named entity recognition . they propose to model nesting entities as multilabels and predict a sequence-to-sequence problem . |
| Outcome: | The proposed methods outperform the state-of-the-art on four corpora . the proposed models also improve on the recently published contextual embeddings . |
Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text. |
| Approach: | They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions . |
| Outcome: | The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources. |
Hierarchical Region Learning for Nested Named Entity Recognition (2020.findings-emnlp)
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| Challenge: | Existing methods to recognize entities recursively from innermost to outermost are based on brute force and two-stage paradigms, often leading to cascaded errors. |
| Approach: | They propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification. |
| Outcome: | Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA show that the proposed framework performs better than state-of-the-art models. |
Named Entity Recognition With Parallel Recurrent Neural Networks (P18-2)
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| Challenge: | Named entity recognition is an important element of natural language understanding . a shift in focus has been on designing better neural architectures for solving NER . |
| Approach: | They propose a new architecture for named entity recognition that uses multiple LSTM units instead of a single LStm component. |
| Outcome: | The proposed architecture achieves state-of-the-art on the CoNLL 2003 NER dataset . |