PromptNER: Prompting For Named Entity Recognition

• URL: http://arxiv.org/abs/2305.15444v2
• Date: Tue, 20 Jun 2023 14:41:38 GMT
• Title: PromptNER: Prompting For Named Entity Recognition
• Authors: Dhananjay Ashok, Zachary C. Lipton
• Abstract summary: We introduce PromptNER, a new state-of-the-art algorithm for few-Shot and cross-domain NER. PromptNER achieves a 4% (absolute) improvement in F1 score on the ConLL dataset, a 9% (absolute) improvement on the GENIA dataset, and a 4% (absolute) improvement on the FewNERD dataset.
• Score: 27.501500279749475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a surprising turn, Large Language Models (LLMs) together with a growing arsenal of prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot solutions to myriad classic NLP problems. However, despite promising early results, these LLM-based few-shot methods remain far from the state of the art in Named Entity Recognition (NER), where prevailing methods include learning representations via end-to-end structural understanding and fine-tuning on standard labeled corpora. In this paper, we introduce PromptNER, a new state-of-the-art algorithm for few-Shot and cross-domain NER. To adapt to any new NER task PromptNER requires a set of entity definitions in addition to the standard few-shot examples. Given a sentence, PromptNER prompts an LLM to produce a list of potential entities along with corresponding explanations justifying their compatibility with the provided entity type definitions. Remarkably, PromptNER achieves state-of-the-art performance on few-shot NER, achieving a 4% (absolute) improvement in F1 score on the ConLL dataset, a 9% (absolute) improvement on the GENIA dataset, and a 4% (absolute) improvement on the FewNERD dataset. PromptNER also moves the state of the art on Cross Domain NER, outperforming prior methods (including those not limited to the few-shot setting), setting a new mark on 3/5 CrossNER target domains, with an average F1 gain of 3%, despite using less than 2% of the available data.

Related papers

• FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios [0.5106912532044251]
  We introduce FsPONER, a novel approach for optimizing few-shot prompts, and evaluate its performance on domain-specific NER datasets. FsPONER consists of three few-shot selection methods based on random sampling, TF-IDF, and a combination of both. In the considered real-world scenarios with data scarcity, FsPONER with TF-IDF surpasses fine-tuned models by approximately 10% in F1 score.
  arXiv  Detail & Related papers  (2024-07-10T20:32:50Z)
• 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)
• 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)
• Enhancing Few-shot NER with Prompt Ordering based Data Augmentation [59.69108119752584]
  We propose a Prompt Ordering based Data Augmentation (PODA) method to improve the training of unified autoregressive generation frameworks. Experimental results on three public NER datasets and further analyses demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-05-19T16:25:43Z)
• Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition [52.46740830977898]
  We propose a novel seq2seq model named GPRL, which formulates the nested NER task as an entity triplet sequence generation process. Experiments on three nested NER datasets demonstrate that GPRL outperforms previous nested NER models.
  arXiv  Detail & Related papers  (2023-05-12T05:55:34Z)
• MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective [57.19660234992812]
  NER model has achieved promising performance on standard NER benchmarks. Recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. We propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective.
  arXiv  Detail & Related papers  (2022-04-09T05:18:20Z)
• Unified Named Entity Recognition as Word-Word Relation Classification [25.801945832005504]
  We present a novel alternative by modeling the unified NER as word-word relation classification, namely W2NER. The architecture resolves the kernel bottleneck of unified NER by effectively modeling the neighboring relations between entity words. Based on the W2NER scheme we develop a neural framework, in which the unified NER is modeled as a 2D grid of word pairs.
  arXiv  Detail & Related papers  (2021-12-19T06:11:07Z)
• CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning [11.289324473201614]
  Existing approaches only learn class-specific semantic features and intermediate representations from source domains. We present CONTaiNER, a novel contrastive learning technique that optimize the inter-token distribution distance for Few-Shot NER. Our experiments in several traditional test domains (OntoNotes, CoNLL'03, WNUT '17, GUM) and a new large scale Few-Shot NER dataset (Few-NERD) demonstrate that CONTaiNER outperforms previous methods by 3%-13% absolute F1 points.
  arXiv  Detail & Related papers  (2021-09-15T21:41:16Z)

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.