Parallel Instance Query Network for Named Entity Recognition

• URL: http://arxiv.org/abs/2203.10545v1
• Date: Sun, 20 Mar 2022 13:01:25 GMT
• Title: Parallel Instance Query Network for Named Entity Recognition
• Authors: Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang
• Abstract summary: Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-specific queries manually to extract entities. We propose Parallel Instance Query Network (PIQN), which sets up global and learnable instance queries to extract entities in a parallel manner.
• Score: 73.30174490672647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-specific queries manually to extract entities. This paradigm suffers from three issues. First, type-specific queries can only extract one type of entities per inference, which is inefficient. Second, the extraction for different types of entities is isolated, ignoring the dependencies between them. Third, query construction relies on external knowledge and is difficult to apply to realistic scenarios with hundreds of entity types. To deal with them, we propose Parallel Instance Query Network (PIQN), which sets up global and learnable instance queries to extract entities from a sentence in a parallel manner. Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel. Instead of being constructed from external knowledge, instance queries can learn their different query semantics during training. For training the model, we treat label assignment as a one-to-many Linear Assignment Problem (LAP) and dynamically assign gold entities to instance queries with minimal assignment cost. Experiments on both nested and flat NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.

Related papers

• OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting [49.655711022673046]
  OneNet is an innovative framework that utilizes the few-shot learning capabilities of Large Language Models (LLMs) without the need for fine-tuning. OneNet is structured around three key components prompted by LLMs: (1) an entity reduction processor that simplifies inputs by summarizing and filtering out irrelevant entities, (2) a dual-perspective entity linker that combines contextual cues and prior knowledge for precise entity linking, and (3) an entity consensus judger that employs a unique consistency algorithm to alleviate the hallucination in the entity linking reasoning.
  arXiv  Detail & Related papers  (2024-10-10T02:45:23Z)
• UQE: A Query Engine for Unstructured Databases [71.49289088592842]
  We investigate the potential of Large Language Models to enable unstructured data analytics. We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
  arXiv  Detail & Related papers  (2024-06-23T06:58:55Z)
• Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains [51.02035914828596]
  We study the task of seed-guided fine-grained entity typing in science and engineering domains. We propose SEType which first enriches the weak supervision by finding more entities for each seen type from an unlabeled corpus. It then matches the enriched entities to unlabeled text to get pseudo-labeled samples and trains a textual entailment model that can make inferences for both seen and unseen types.
  arXiv  Detail & Related papers  (2024-01-23T22:36:03Z)
• Entity Matching using Large Language Models [3.7277730514654555]
  This paper investigates using generative large language models (LLMs) as a less task-specific training data-dependent alternative to PLM-based matchers. We show that GPT4 can generate structured explanations for matching decisions and can automatically identify potential causes of matching errors.
  arXiv  Detail & Related papers  (2023-10-17T13:12:32Z)
• Few-Shot Nested Named Entity Recognition [4.8693196802491405]
  This paper is the first one dedicated to studying the few-shot nested NER task. We propose a Biaffine-based Contrastive Learning (BCL) framework to learn contextual dependency to distinguish nested entities. The BCL outperformed three baseline models on the 1-shot and 5-shot tasks in terms of F1 score.
  arXiv  Detail & Related papers  (2022-12-02T03:42:23Z)
• Learning Equivariant Segmentation with Instance-Unique Querying [47.52528819153683]
  We devise a new training framework that boosts query-based models through discriminative query embedding learning. Our algorithm uses the queries to retrieve the corresponding instances from the whole training dataset. On top of four famous, query-based models, our training algorithm provides significant performance gains.
  arXiv  Detail & Related papers  (2022-10-03T13:14:00Z)
• MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations [28.28940043641958]
  We propose a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a searching method. Our method achieves the state-of-the-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets.
  arXiv  Detail & Related papers  (2021-09-13T05:51:45Z)
• LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention [37.111204321059084]
  We propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. Our model is trained using a new pretraining task based on the masked language model of BERT. We also propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer.
  arXiv  Detail & Related papers  (2020-10-02T15:38:03Z)
• Autoregressive Entity Retrieval [55.38027440347138]
  Entities are at the center of how we represent and aggregate knowledge. The ability to retrieve such entities given a query is fundamental for knowledge-intensive tasks such as entity linking and open-domain question answering. We propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion.
  arXiv  Detail & Related papers  (2020-10-02T10:13:31Z)

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.