| Challenge: | Existing models that assume that mentions are non-overlapping spans in text may not perform well in practice. |
| Approach: | They propose a segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. |
| Outcome: | The proposed representation achieves state-of-the-art performance in three benchmark datasets annotated with overlapping mentions. |
Similar Papers
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. |
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing (2022.acl-long)
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| Challenge: | Existing models struggle to handle hard mentions due to insufficient contexts, limiting their overall typing performance. |
| Approach: | They propose to exploit sibling mentions to enhance the mention representations by adding unseen test mentions as new nodes for inference. |
| Outcome: | The proposed model outperforms ten strong baseline models and outperformed strong baselines. |
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network (2021.acl-short)
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| Challenge: | Existing approaches to entity linking represent each entity with a single vector, but instead use a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Approach: | They propose an instance-based nearest neighbor approach to entity linking that allows for a contextualized mention-encoder to learn to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Outcome: | The proposed approach outperforms all other systems on two multilingual benchmarks and is simpler to train and interpretable. |
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. |
Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities (D19-1)
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| Challenge: | Named entity recognition (NER) aims at identifying shallow semantic elements in text. |
| Approach: | They propose a neural two-stage approach to recognizing discontiguous and overlapping entities by decomposing the problem into two subtasks. |
| Outcome: | The proposed model achieves state-of-the-art in a standard dataset even without external features. |
Improving Entity Linking by Modeling Latent Relations between Mentions (P18-1)
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| Challenge: | Entity linking systems often exploit relations between textual mentions to decide if the linking decisions are compatible. |
| Approach: | They treat relations as latent variables while optimizing the neural entity-linking model without supervision. |
| Outcome: | The proposed model outperforms its relation-agnostic version and significantly outperformed its relational version. |
Towards Improving Neural Named Entity Recognition with Gazetteers (P19-1)
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
| Approach: | They propose to use external gazetteers to efficiently access annotated data to generalize beyond the annotation of entities. |
| Outcome: | The proposed model can access external gazetteers while avoiding the effort to design hand-crafted features. |
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. |
Neural Models for Reasoning over Multiple Mentions Using Coreference (N18-2)
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| Challenge: | Existing Recurrent Neural Network (RNN) layers are biased towards short-term dependencies and hence not suited to such tasks. |
| Approach: | They propose a recurrent layer which is instead biased towards coreferent dependencies and uses coreference annotations extracted from an external system to connect entity mentions belonging to the same cluster. |
| Outcome: | The proposed layer improves performance on Wikihop, LAMBADA and the bAbi AI datasets with large gains when training data is scarce. |
Pre-training Mention Representations in Coreference Models (2020.emnlp-main)
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| Challenge: | Existing methods to improve coreference resolution use labeled data. |
| Approach: | They propose two self-supervised tasks that are closely related to coreference resolution to improve mention representation. |
| Outcome: | The proposed models improve mention representations by learning them on a GAP dataset. |