| Challenge: | Existing methods for named entity recognition (NER) use labeled data for both source and target domains. |
| Approach: | They propose to use language modeling as a bridge between NER domains to perform cross-domain and cross-task knowledge transfer. |
| Outcome: | The proposed method extracts domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while giving state-of-the-art results among supervised domain adapters. |
Similar Papers
Improving Named Entity Recognition via Bridge-based Domain Adaptation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for named entity recognition use pre-training language models to represent words, leading to entity type misclassification. |
| Approach: | They propose a model-agnostic framework called MoCL for cross-domain named entity recognition to refine the original representations and combine it with two distinct cross- domain NER methods and two pre-training language models to explore its generalization ability. |
| Outcome: | The proposed framework is model-agnostic and can be used to generalize and refine existing models. |
Cross-domain Named Entity Recognition via Graph Matching (2022.findings-acl)
Copied to clipboard
| Challenge: | Empirical results show that our method outperforms a series of transfer learning, multitask learning, and few-shot learning methods due to the data scarcity in the real-world scenario. |
| Approach: | They propose to model the label relationship as a probability distribution and construct label graphs in both source and target label spaces. |
| Outcome: | Empirical results show that the proposed method outperforms transfer learning, multi-task learning, and few-shot learning methods on four datasets. |
Neural Adaptation Layers for Cross-domain Named Entity Recognition (D18-1)
Copied to clipboard
| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
| Approach: | They propose to use existing neural architectures to adapt to new domains without retraining . they propose to add adaptation layers to existing neural models to minimize re-training based on source data. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art methods on social media domains. |
Data Augmentation for Cross-Domain Named Entity Recognition (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for named entity recognition focus on augmenting in-domain data in low-resource scenarios where annotated data is limited. |
| Approach: | They propose a neural architecture to transform data from high-resource to low-resourced domains by learning the patterns in the text that differentiate them. |
| Outcome: | The proposed approach improves on high-resource domain representations over high- and low-resourced domains. |
A Label-Aware Autoregressive Framework for Cross-Domain NER (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) focus on reducing discrepancy between tokens and tokens, but transfer of valuable label information is often not considered or ignored. |
| Approach: | They propose a framework that borrows entity information from the source domain to enhance NER in the target domain. |
| Outcome: | The proposed model improves over the state-of-the-art model on several datasets. |
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (D19-1)
Copied to clipboard
| Challenge: | Named entity recognition models are challenging for languages with little training data. |
| Approach: | They propose a simple and efficient neural architecture for cross-lingual named entity recognition models. |
| Outcome: | The proposed model achieves competitive performance with the state-of-the-art on two transferable factors: sequential order and multilingual embedding. |
T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition (2021.eacl-demos)
Copied to clipboard
| Challenge: | Language model (LM) pretraining has led to consistent improvements in many downstream tasks, including named entity recognition (NER). |
| Approach: | They propose a Python library for NER LM finetuning that facilitates cross-domain and cross-lingual generalization of LMs finetuned on NER. |
| Outcome: | The proposed library outperforms LMs trained on NERs in cross-domain and cross-lingual generalization tests on nine datasets. |
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to Named Entity Recognition (NER) are limited in labeled resources and domain shift. |
| Approach: | They propose a progressive domain adaptation knowledge distillation approach to adapt high-resource domains to low-resourced target domains by employing three components to achieve superior domain adaptability. |
| Outcome: | The proposed approach can adapt high-resource domains to low-resourced target domains even if they are diverse in terms and writing styles. |
Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model (2023.eacl-main)
Copied to clipboard
Mayank Kulkarni, Daniel Preotiuc-Pietro, Karthik Radhakrishnan, Genta Indra Winata, Shijie Wu, Lingjue Xie, Shaohua Yang
| Challenge: | Named Entity Recognition is a key task whose performance is sensitive to genre and language. |
| Approach: | They propose a setup for Named Entity Recognition which includes multi-domain and multilingual training and evaluation across 13 domains and 4 languages. |
| Outcome: | The proposed model improves on 13 domains and 4 languages across 13 domain and 4 language domains. |
Domain-Specific NER via Retrieving Correlated Samples (2022.coling-1)
Copied to clipboard
| Challenge: | Successful Named Entity Recognition models fail on texts from some special domains, for example, Chinese addresses and e-commerce titles. |
| Approach: | They propose to enhance NER models with correlated samples to help the text understanding . they draw correlated texts by the sparse BM25 retriever from large-scale in-domain unlabeled data . |
| Outcome: | Empirical results show that NER models can be enhanced with correlated samples . the proposed model can be used to reason out the correct answer on hard cases . |