Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition

• URL: http://arxiv.org/abs/2204.12732v1
• Date: Wed, 27 Apr 2022 06:58:45 GMT
• Title: Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition
• Authors: Shuhui Wu, Yongliang Shen, Zeqi Tan, Weiming Lu
• Abstract summary: We present the Propose-and-Refine Network (PnRNet), a two-stage set prediction network for nested NER. In the propose stage, we use a span-based predictor to generate some coarse entity predictions as entity proposals. In the refine stage, proposals interact with each other, and richer contextual information is incorporated into the proposal representations. Experiments show that PnRNet achieves state-of-the-art performance on four nested NER datasets and one flat NER dataset.
• Score: 13.010064498077863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nested named entity recognition (nested NER) is a fundamental task in natural language processing. Various span-based methods have been proposed to detect nested entities with span representations. However, span-based methods do not consider the relationship between a span and other entities or phrases, which is helpful in the NER task. Besides, span-based methods have trouble predicting long entities due to limited span enumeration length. To mitigate these issues, we present the Propose-and-Refine Network (PnRNet), a two-stage set prediction network for nested NER. In the propose stage, we use a span-based predictor to generate some coarse entity predictions as entity proposals. In the refine stage, proposals interact with each other, and richer contextual information is incorporated into the proposal representations. The refined proposal representations are used to re-predict entity boundaries and classes. In this way, errors in coarse proposals can be eliminated, and the boundary prediction is no longer constrained by the span enumeration length limitation. Additionally, we build multi-scale sentence representations, which better model the hierarchical structure of sentences and provide richer contextual information than token-level representations. Experiments show that PnRNet achieves state-of-the-art performance on four nested NER datasets and one flat NER dataset.

Related papers

• ACE: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
  We propose a pioneering generAtive Cross-modal rEtrieval framework (ACE) for end-to-end cross-modal retrieval. ACE achieves state-of-the-art performance in cross-modal retrieval and outperforms the strong baselines on Recall@1 by 15.27% on average.
  arXiv  Detail & Related papers  (2024-06-25T12:47:04Z)
• A Boundary Offset Prediction Network for Named Entity Recognition [9.885278527023532]
  Named entity recognition (NER) is a fundamental task in natural language processing that aims to identify and classify named entities in text. We propose a novel approach for NER, named the Boundary Offset Prediction Network (BOPN), which predicts the boundary offsets between candidate spans and their nearest entity spans. Our method integrates entity type and span representations to generate type-aware boundary offsets instead of using entity types as detection targets.
  arXiv  Detail & Related papers  (2023-10-23T05:04:07Z)
• NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval [49.827932299460514]
  We argue that capabilities provided by large language models are not the end of NER research, but rather an exciting beginning. We present three variants of the NER task, together with a dataset to support them. We provide a large, silver-annotated corpus of 4 million paragraphs covering 500 entity types.
  arXiv  Detail & Related papers  (2023-10-22T12:23:00Z)
• Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach [50.12455129619845]
  Named Entity Recognition (NER) aims to extract and classify entity mentions in the text into pre-defined types. We propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER.
  arXiv  Detail & Related papers  (2023-09-20T03:15:05Z)
• PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search [56.81939214465558]
  We propose PromptNER: a novel prompting method for few-shot NER via k nearest neighbor search. We use prompts that contains entity category information to construct label prototypes, which enables our model to fine-tune with only the support set. Our approach achieves excellent transfer learning ability, and extensive experiments on the Few-NERD and CrossNER datasets demonstrate that our model achieves superior performance over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-20T15:47:59Z)
• Enriching Relation Extraction with OpenIE [70.52564277675056]
  Relation extraction (RE) is a sub-discipline of information extraction (IE) In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models.
  arXiv  Detail & Related papers  (2022-12-19T11:26:23Z)
• SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition [45.012327072558975]
  Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. We propose a seminal span-based prototypical network (SpanProto) that tackles few-shot NER via a two-stage approach. In the span extraction stage, we transform the sequential tags into a global boundary matrix, enabling the model to focus on the explicit boundary information. For mention classification, we leverage prototypical learning to capture the semantic representations for each labeled span and make the model better adapt to novel-class entities.
  arXiv  Detail & Related papers  (2022-10-17T12:59:33Z)
• InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER [31.32381919473188]
  We propose a multi-task instruction-based generative framework, named InstructionNER, for low-resource named entity recognition. Specifically, we reformulate the NER task as a generation problem, which enriches source sentences with task-specific instructions and answer options, then inferences the entities and types in natural language. Experimental results show that our method consistently outperforms other baselines on five datasets in few-shot settings.
  arXiv  Detail & Related papers  (2022-03-08T07:56:36Z)
• A Sequence-to-Set Network for Nested Named Entity Recognition [38.05786148160635]
  We propose a novel sequence-to-set neural network for nested NER. We use a non-autoregressive decoder to predict the final set of entities in one pass. Experimental results show that our proposed model achieves state-of-the-art on three nested NER corpora.
  arXiv  Detail & Related papers  (2021-05-19T03:10:04Z)
• Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition [9.809157050048375]
  We propose a two-stage entity identifier for named entity recognition. First, we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training.
  arXiv  Detail & Related papers  (2021-05-14T12:52:34Z)
• VL-NMS: Breaking Proposal Bottlenecks in Two-Stage Visual-Language Matching [75.71523183166799]
  The prevailing framework for matching multimodal inputs is based on a two-stage process. We argue that these methods overlook an obvious emphmismatch between the roles of proposals in the two stages. We propose VL-NMS, which is the first method to yield query-aware proposals at the first stage.
  arXiv  Detail & Related papers  (2021-05-12T13:05:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.