Cascaded Models for Better Fine-Grained Named Entity Recognition

• URL: http://arxiv.org/abs/2009.07317v1
• Date: Tue, 15 Sep 2020 18:41:29 GMT
• Title: Cascaded Models for Better Fine-Grained Named Entity Recognition
• Authors: Parul Awasthy and Taesun Moon and Jian Ni and Radu Florian
• Abstract summary: We present a cascaded approach to labeling fine-grained NER, applying to a newly released fine-grained NER dataset. We show that performance can be improved by about 20 F1 absolute, as compared with the straightforward model built on the full fine-grained types.
• Score: 10.03287972980716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction. Much of the NER research has been done on datasets with few classes of entity types (e.g. PER, LOC, ORG, MISC), but many real world applications (disaster relief, complex event extraction, law enforcement) can benefit from a larger NER typeset. More recently, datasets were created that have hundreds to thousands of types of entities, sparking new lines of research (Sekine, 2008;Ling and Weld, 2012; Gillick et al., 2014; Choiet al., 2018). In this paper we present a cascaded approach to labeling fine-grained NER, applying to a newly released fine-grained NER dataset that was used in the TAC KBP 2019 evaluation (Ji et al., 2019), inspired by the fact that training data is available for some of the coarse labels. Using a combination of transformer networks, we show that performance can be improved by about 20 F1 absolute, as compared with the straightforward model built on the full fine-grained types, and show that, surprisingly, using course-labeled data in three languages leads to an improvement in the English data.

Related papers

• Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition [40.23783832224238]
  We present B2NERD, a cohesive and efficient dataset for Open NER. We detect inconsistent entity definitions across datasets and clarify them by distinguishable label names to construct a universal taxonomy of 400+ entity types. Our B2NER models, trained on B2NERD, outperform GPT-4 by 6.8-12.0 F1 points and surpass previous methods in 3 out-of-domain benchmarks across 15 datasets and 6 languages.
  arXiv  Detail & Related papers  (2024-06-17T03:57:35Z)
• 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)
• 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)
• 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)
• Dynamic Conceptional Contrastive Learning for Generalized Category Discovery [76.82327473338734]
  Generalized category discovery (GCD) aims to automatically cluster partially labeled data. Unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. We propose a Dynamic Conceptional Contrastive Learning framework, which can effectively improve clustering accuracy.
  arXiv  Detail & Related papers  (2023-03-30T14:04:39Z)
• T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition [9.928025283928282]
  T-NER is a Python library for NER LM finetuning. We show the potential of the library by compiling nine public NER datasets into a unified format. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.
  arXiv  Detail & Related papers  (2022-09-09T15:00:38Z)
• A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity Recognition [5.030581940990434]
  Cross-lingual Named Entity Recognition (NER) has recently become a research hotspot because it can alleviate the data-hungry problem for low-resource languages. In this paper, we describe our novel dual-contrastive framework ConCNER for cross-lingual NER under the scenario of limited source-language labeled data.
  arXiv  Detail & Related papers  (2022-04-02T07:59:13Z)
• Trustable Co-label Learning from Multiple Noisy Annotators [68.59187658490804]
  Supervised deep learning depends on massive accurately annotated examples. A typical alternative is learning from multiple noisy annotators. This paper proposes a data-efficient approach, called emphTrustable Co-label Learning (TCL)
  arXiv  Detail & Related papers  (2022-03-08T16:57:00Z)
• AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER [2.739898536581301]
  We design an adversarial learning framework in which an encoder learns entity domain knowledge from labeled source-language data. We show that the proposed method benefits strongly from this data selection process and outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-04T07:17:18Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)

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.