Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach

• URL: http://arxiv.org/abs/2309.11027v1
• Date: Wed, 20 Sep 2023 03:15:05 GMT
• Title: Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach
• Authors: Yibo Wang, Wenting Zhao, Yao Wan, Zhongfen Deng, Philip S. Yu
• Abstract summary: 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.
• Score: 50.12455129619845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Named Entity Recognition (NER) aims to extract and classify entity mentions in the text into pre-defined types (e.g., organization or person name). Recently, many works have been proposed to shape the NER as a machine reading comprehension problem (also termed MRC-based NER), in which entity recognition is achieved by answering the formulated questions related to pre-defined entity types through MRC, based on the contexts. However, these works ignore the label dependencies among entity types, which are critical for precisely recognizing named entities. In this paper, we propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER. We decompose MRC-based NER into multiple tasks and use a self-attention module to capture label dependencies. Comprehensive experiments on both nested NER and flat NER datasets are conducted to validate the effectiveness of the proposed Multi-NER. Experimental results show that Multi-NER can achieve better performance on all datasets.

Related papers

• Advancing Grounded Multimodal Named Entity Recognition via LLM-Based Reformulation and Box-Based Segmentation [46.9782192992495]
  Grounded Multimodal Named Entity Recognition (GMNER) task aims to identify named entities, entity types and their corresponding visual regions. We propose RiVEG, a unified framework that reformulates GMNER into a joint MNER-VE-VG task by leveraging large language models.
  arXiv  Detail & Related papers  (2024-06-11T13:52:29Z)
• Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning [32.62763647036567]
  Few-shot named entity recognition can identify new types of named entities based on a few labeled examples. We propose the Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning (MsFNER) MsFNER splits the general NER into two stages: entity-span detection and entity classification.
  arXiv  Detail & Related papers  (2024-04-10T12:31:09Z)
• In-Context Learning for Few-Shot Nested Named Entity Recognition [53.55310639969833]
  We introduce an effective and innovative ICL framework for the setting of few-shot nested NER. We improve the ICL prompt by devising a novel example demonstration selection mechanism, EnDe retriever. In EnDe retriever, we employ contrastive learning to perform three types of representation learning, in terms of semantic similarity, boundary similarity, and label similarity.
  arXiv  Detail & Related papers  (2024-02-02T06:57:53Z)
• 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)
• MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition [75.87566793111066]
  We propose a noise-robust prototype network named MProto for the DS-NER task. MProto represents each entity type with multiple prototypes to characterize the intra-class variance. To mitigate the noise from incomplete labeling, we propose a novel denoised optimal transport (DOT) algorithm.
  arXiv  Detail & Related papers  (2023-10-12T13:02:34Z)
• Dynamic Named Entity Recognition [5.9401550252715865]
  We introduce a new task: Dynamic Named Entity Recognition (DNER) DNER provides a framework to better evaluate the ability of algorithms to extract entities by exploiting the context. We evaluate baseline models and present experiments reflecting issues and research axes related to this novel task.
  arXiv  Detail & Related papers  (2023-02-16T15:50:02Z)
• MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding [21.49274082010887]
  Multimodal named entity recognition (MNER) is a critical step in information extraction. We propose a novel end-to-end framework named MNER-QG that can simultaneously perform MRC-based MRC-based named entity recognition and query grounding.
  arXiv  Detail & Related papers  (2022-11-27T06:10:03Z)
• Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF [53.55504611255664]
  We propose a novel method, referred to as ASAC, to solve the dilemma caused by the nested phenomenon. The proposed method contains two key modules: the adaptive shared (AS) part and the attentive conditional random field (ACRF) module. Our model could learn better entity representations by capturing the implicit distinctions and relationships between different categories of entities.
  arXiv  Detail & Related papers  (2022-11-09T09:23:56Z)
• Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning [80.36076044023581]
  We present an efficient bi-encoder framework for named entity recognition (NER) We frame NER as a metric learning problem that maximizes the similarity between the vector representations of an entity mention and its type. A major challenge to this bi-encoder formulation for NER lies in separating non-entity spans from entity mentions.
  arXiv  Detail & Related papers  (2022-08-30T23:19:04Z)
• NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension [6.3066273316843775]
  We propose posing NER as a multi-question MRC task, where multiple questions are considered at the same time for a single text. We show that our proposed architecture leads to average 2.5 times faster training and 2.3 times faster inference as compared to NER-SQMRC framework based models.
  arXiv  Detail & Related papers  (2022-05-12T06:54:03Z)

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.