| Challenge: | Existing named entity recognition models use gazetteers to improve performance, but they are limited in coverage and do not exist in low-resource languages. |
| Approach: | They propose a method that integrates Wikipedia information into named entity models by cross-lingual entity linking. |
| Outcome: | The proposed method improves on four low-resource languages with Wikipedia . it incorporates available information from english knowledge bases into neural models . |
Similar Papers
Towards Improving Neural Named Entity Recognition with Gazetteers (P19-1)
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
| Approach: | They propose to use external gazetteers to efficiently access annotated data to generalize beyond the annotation of entities. |
| Outcome: | The proposed model can access external gazetteers while avoiding the effort to design hand-crafted features. |
The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English (2021.findings-acl)
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| Challenge: | Recent papers introduce methods to incorporate gazetteer features and entity segmentation techniques in neural named entity recognition models. |
| Approach: | They propose to integrate gazetteer features and entity segmentation techniques into neural named entity recognition models. |
| Outcome: | The proposed methods improve entity segmentation and not just entity typing. |
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers (D19-1)
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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
| Approach: | They propose a method for bootstrapping named entity recognition models in under-resourced languages . they use cross-lingual transfer learning and targeted annotation of only uncertain entities . |
| Outcome: | The proposed method achieves competitive accuracy with just one-tenth of training data. |
GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input (2021.naacl-main)
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| Challenge: | Named Entity Recognition (NER) is difficult in real-world settings due to short texts, emerging entities, and complex entities. |
| Approach: | They propose a flexible Gazetteer Representation encoder and a Mixture-of-Experts gating network for gazetteer knowledge integration. |
| Outcome: | The proposed approach shows large gains (up to +49% F1) in recognizing difficult entities compared to baselines. |
A Neural Multi-digraph Model for Chinese NER with Gazetteers (P19-1)
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| Challenge: | Existing approaches to incorporating gazetteers into NER systems rely on manually defined selection strategies or handcrafted templates, which may not lead to optimal effectiveness. |
| Approach: | They propose to use graph neural networks to automatically learn how to incorporate multiple gazetteers into an NER system by capturing the information that the gazetteer offers. |
| Outcome: | The proposed model outperforms existing methods on Chinese NER datasets while incorporating rich gazetteer information while resolving ambiguities. |
Robust Lexical Features for Improved Neural Network Named-Entity Recognition (C18-1)
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| Challenge: | Named-Entity Recognition (NER) uses word embeddings to extend, rather than replace, hand-crafted features. |
| Approach: | They propose to embed words and entity types into a low-dimensional vector space and compute a feature vector representing each word offline. |
| Outcome: | The proposed representations outperform existing models and achieve state-of-the-art performance. |
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (D19-1)
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| 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. |
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition (2022.naacl-main)
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| Challenge: | Named entity recognition (NER) models trained on CoNLL do not transfer well to other domains, even within the same language. |
| Approach: | They propose a token-level gating layer to augment pre-trained multilingual transformers with gazetteers containing named entities (NE) from a target language or domain. |
| Outcome: | The proposed model improves on cross-lingual transfer with an F1 score of 92.92 for English and an average of 89.43 across all languages in CoNLL. |
Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition (D19-1)
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| Challenge: | Named entity recognition (NER) is a fundamental NLP task. |
| Approach: | They propose a gazetteer-based attentive neural network which can enhance region-based NER . they first model the mention-context association and then an auxiliary gazetteers . |
| Outcome: | The proposed approach can achieve state-of-the-art on ACE2005 named entity recognition benchmark. |
Neural Cross-Lingual Named Entity Recognition with Minimal Resources (D18-1)
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| Challenge: | Named-entity recognition (NER) models are highly dependent on large amounts of labeled data. |
| Approach: | They propose a method that finds translations based on bilingual word embeddings . they also propose 'self-attention' which allows for a degree of flexibility with respect to word order . |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance on common languages with lower resource requirements than previous approaches. |