ToNER: Type-oriented Named Entity Recognition with Generative Language Model

• URL: http://arxiv.org/abs/2404.09145v2
• Date: Tue, 11 Jun 2024 14:05:03 GMT
• Title: ToNER: Type-oriented Named Entity Recognition with Generative Language Model
• Authors: Guochao Jiang, Ziqin Luo, Yuchen Shi, Dixuan Wang, Jiaqing Liang, Deqing Yang,
• Abstract summary: We propose a novel NER framework, namely ToNER based on a generative model. In ToNER, a type matching model is proposed at first to identify the entity types most likely to appear in the sentence. We append a multiple binary classification task to fine-tune the generative model's encoder, so as to generate the refined representation of the input sentence.
• Score: 14.11486479935094
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task. It has also been found that the information related to entities, such as entity types, can prompt a model to achieve NER better. However, it is not easy to determine the entity types indeed existing in the given sentence in advance, and inputting too many potential entity types would distract the model inevitably. To exploit entity types' merit on promoting NER task, in this paper we propose a novel NER framework, namely ToNER based on a generative model. In ToNER, a type matching model is proposed at first to identify the entity types most likely to appear in the sentence. Then, we append a multiple binary classification task to fine-tune the generative model's encoder, so as to generate the refined representation of the input sentence. Moreover, we add an auxiliary task for the model to discover the entity types which further fine-tunes the model to output more accurate results. Our extensive experiments on some NER benchmarks verify the effectiveness of our proposed strategies in ToNER that are oriented towards entity types' exploitation.

Related papers

• 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)
• Entity Disambiguation via Fusion Entity Decoding [68.77265315142296]
  We propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. We observe +1.5% improvements in end-to-end entity linking in the GERBIL benchmark compared with EntQA.
  arXiv  Detail & Related papers  (2024-04-02T04:27:54Z)
• 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)
• 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)
• Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition [35.44123819012004]
  Named entity recognition (NER) is an important research problem in natural language processing. This paper proposes a multi-task Transformer, which incorporates an entity boundary detection task into the named entity recognition task.
  arXiv  Detail & Related papers  (2023-03-20T05:11:22Z)
• Simple Questions Generate Named Entity Recognition Datasets [18.743889213075274]
  This work introduces an ask-to-generate approach, which automatically generates NER datasets by asking simple natural language questions. Our models largely outperform previous weakly supervised models on six NER benchmarks across four different domains. Formulating the needs of NER with natural language also allows us to build NER models for fine-grained entity types such as Award.
  arXiv  Detail & Related papers  (2021-12-16T11:44:38Z)
• Injecting Entity Types into Entity-Guided Text Generation [39.96689831978859]
  In this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. Our model has a multi-step decoder that injects the entity types into the process of entity mention generation. Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines.
  arXiv  Detail & Related papers  (2020-09-28T15:19:28Z)
• Interpretable Entity Representations through Large-Scale Typing [61.4277527871572]
  We present an approach to creating entity representations that are human readable and achieve high performance out of the box. Our representations are vectors whose values correspond to posterior probabilities over fine-grained entity types. We show that it is possible to reduce the size of our type set in a learning-based way for particular domains.
  arXiv  Detail & Related papers  (2020-04-30T23:58:03Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

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.