CrossNER: Evaluating Cross-Domain Named Entity Recognition (original) (raw)
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Zero-Resource Cross-Domain Named Entity Recognition
Proceedings of the 5th Workshop on Representation Learning for NLP, 2020
Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains. However, collecting data for low-resource target domains is not only expensive but also time-consuming. Hence, we propose a cross-domain NER model that does not use any external resources. We first introduce a Multi-Task Learning (MTL) by adding a new objective function to detect whether tokens are named entities or not. We then introduce a framework called Mixture of Entity Experts (MoEE) to improve the robustness for zero-resource domain adaptation. Finally, experimental results show that our model outperforms strong unsupervised cross-domain sequence labeling models, and the performance of our model is close to that of the state-of-theart model which leverages extensive resources.
arXiv (Cornell University), 2022
Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-domain examples. This paper proposes a two-step rationale-centric data augmentation method to improve the model's generalization ability. Results on several datasets show that our model-agnostic method significantly improves the performance of crossdomain NER tasks compared to previous stateof-the-art methods, including the data augmentation and prompt-tuning methods. Our codes are available at https://github.com/ lifan-yuan/FactMix. * Equal contribution. Random order of the authorship. † Work done at Westlake University as an intern. Input: Germany imported 47,600 sheep from Britain last year nearly half of total imports.
DOMAIN-TRANSFERABLE METHOD FOR NAMED ENTITY RECOGNITION TASK
Named Entity Recognition (NER) is a fundamental task in the fields of natural language processing and information extraction. NER has been widely used as a standalone tool or an essential component in a variety of applications such as question answering, dialogue assistants and knowledge graphs development. However, training reliable NER models requires a large amount of labelled data which is expensive to obtain, particularly in specialized domains. This paper describes a method to learn a domain-specific NER model for an arbitrary set of named entities when domain-specific supervision is not available. We assume that the supervision can be obtained with no human effort, and neural models can learn from each other. The code, data and models are publicly available.
Entity Extraction in Low Resource Domains with Selective Pre-training of Large Language Models
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Transformer-based language models trained on large natural language corpora have been very useful in downstream entity extraction tasks. However, they often result in poor performances when applied to domains that are different from those they are pretrained on. Continued pretraining using unlabeled data from target domains can help improve the performances of these language models on the downstream tasks. However, using all of the available unlabeled data for pretraining can be time-intensive; also, it can be detrimental to the performance of the downstream tasks, if the unlabeled data is not aligned with the data distribution for the target tasks. Previous works employed external supervision in the form of ontologies for selecting appropriate data samples for pretraining, but external supervision can be quite hard to obtain in low-resource domains. In this paper, we introduce effective ways to select data from unlabeled corpora of target domains for language model pretraining to improve the performances in target entity extraction tasks. Our data selection strategies do not require any external supervision. We conduct extensive experiments for the task of named entity recognition (NER) on seven different domains and show that language models pretrained on target domain unlabeled data obtained using our data selection strategies achieve better performances compared to those using data selection strategies in previous works that use external supervision. We also show that these pretrained language models using our data selection strategies outperform those pretrained on all of the available unlabeled target domain data.
Few-Shot Named Entity Recognition: An Empirical Baseline Study
2021
This paper presents an empirical study to efficiently build named entity recognition (NER) systems when a small amount of in-domain labeled data is available. Based upon recent Transformer-based self-supervised pre-trained language models (PLMs), we investigate three orthogonal schemes to improve model generalization ability in few-shot settings: (1) metalearning to construct prototypes for different entity types, (2) task-specific supervised pretraining on noisy web data to extract entityrelated representations and (3) self-training to leverage unlabeled in-domain data. On 10 public NER datasets, we perform extensive empirical comparisons over the proposed schemes and their combinations with various proportions of labeled data, our experiments show that (i) in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned using domain labels. (ii) We create new state-of-theart results on both few-shot and training-free settings compared with existing methods.
Transfer Learning and Domain Adaptation for Named-Entity Recognition
2020
Transfer learning is a technique that is widely used in machine learning tasks when the availability of data for a task is limited and not enough to train a model. Transfer learning consists of primarily two major phases: pre-training and fine-tuning. During pre-training, a model is trained on a different but similar corpus which has a large amount of data in it. This model is further fine-tuned on the target task using the limited data available. Selecting the appropriate pre-training corpus is vital to avoid negative transfer learning that can harm a target task and impede the transfer of knowledge. Transfer learning is being used widely in industrial applications where the amount of labelled data is limited. This paper applies transfer learning to the task of named-entity recognition and investigates the effectiveness of transfer learning in a stacked bi-LSTM architecture with fastText word embedding.
Using Domain Knowledge for Low Resource Named Entity Recognition
In recent years, named entity recognition has always been a popular research in the field of natural language processing, while traditional deep learning methods require a large amount of labeled data for model training, which makes them not suitable for areas where labeling resources are scarce. In addition, the existing cross-domain knowledge transfer methods need to adjust the entity labels for different fields, so as to increase the training cost. To solve these problems, enlightened by a processing method of Chinese named entity recognition, we propose to use domain knowledge to improve the performance of named entity recognition in areas with low resources. The domain knowledge mainly applied by us is domain dictionary and domain labeled data. We use dictionary information for each word to strengthen its word embedding and domain labeled data to reinforce the recognition effect. The proposed model avoids large-scale data adjustments in different domains while handling named entities recognition with low resources. Experiments demonstrate the effectiveness of our method, which has achieved impressive results on the data set in the field of scientific and technological equipment, and the F1 score has been significantly improved compared with many other baseline methods.
Named Entity Recognition for Novel Types by Transfer Learning
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016
In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types and a small amount of in-domain training data, we use transfer learning to learn a domain-specific NE model. That is, the novelty in the task setup is that we assume not just domain mismatch, but also label mismatch.
Few-Shot Named Entity Recognition: A Comprehensive Study
ArXiv, 2020
This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of indomain labeled data is available. Based upon recent Transformer-based self-supervised pretrained language models (PLMs), we investigate three orthogonal schemes to improve the model generalization ability for few-shot settings: (1) meta-learning to construct prototypes for different entity types, (2) supervised pre-training on noisy web data to extract entity-related generic representations and (3) self-training to leverage unlabeled in-domain data. Different combinations of these schemes are also considered. We perform extensive empirical comparisons on 10 public NER datasets with various proportions of labeled data, suggesting useful insights for future research. Our experiments show that (i) in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned on...
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019
Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lowerresourced languages. However, there are now several proposed approaches involving either cross-lingual transfer learning, which learns from other highly resourced languages, or active learning, which efficiently selects effective training data based on model predictions. This paper poses the question: given this recent progress, and limited human annotation, what is the most effective method for efficiently creating high-quality entity recognizers in under-resourced languages? Based on extensive experimentation using both simulated and real human annotation, we find a dualstrategy approach best, starting with a crosslingual transferred model, then performing targeted annotation of only uncertain entity spans in the target language, minimizing annotator effort. Results demonstrate that cross-lingual transfer is a powerful tool when very little data can be annotated, but an entity-targeted annotation strategy can achieve competitive accuracy quickly, with just one-tenth of training data. The code is publicly available here. 1