Papers with Chinese NER
Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition (2021.acl-short)
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| Challenge: | Existing approaches to Chinese Named Entity Recognition (NER) lack explicit word boundary and tenses information. |
| Approach: | They propose a boundary enhanced approach for Chinese Named Entity Recognition . they add an additional Graph Attention Network(GAT) layer to capture internal dependency of phrases . |
| Outcome: | The proposed approach improves Chinese Named Entity Recognition (NER) on OntoNotes and Weibo corpora. |
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism (D18-1)
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| Challenge: | Existing methods for named entity recognition (NER) do not exploit word boundary information from CWS or cannot filter the specific information of CWS. |
| Approach: | They propose to exploit task-shared boundary information to make full use of Chinese NER task and Chinese word segmentation (CWS) . |
| Outcome: | The proposed model significantly outperforms other state-of-the-art methods on two widely used datasets. |
A Discriminative Neural Model for Cross-Lingual Word Alignment (D19-1)
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| Challenge: | a novel word alignment model for machine translation has been developed for a number of languages . explicit word-to-word alignments have largely been lost in neural MT systems . |
| Approach: | They propose a discriminative word alignment model which integrates into a Transformer-based machine translation model. |
| Outcome: | The proposed model performs better on Chinese and Arabic alignments than standard models. |
MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition (2021.acl-long)
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| Challenge: | Named Entity Recognition (NER) is a sequence tagging task that extracts named entities from unstructured text. |
| Approach: | They propose to integrate Chinese character features with radical-level embedding to improve Chinese NER by integrating Chinese character information. |
| Outcome: | The proposed method can improve Chinese Named Entity Recognition (NER) on well-known datasets. |
Improving Model Generalization: A Chinese Named Entity Recognition Case Study (2021.acl-short)
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| Challenge: | Named Entity Recognition (NER) is a fundamental building block for various downstream natural language processing tasks due to the ambiguous word boundaries and complex composition. |
| Approach: | They propose to resample entities within the same category to encourage a model to leverage both name and context knowledge in the training process. |
| Outcome: | The proposed method significantly improves a model’s ability to detect unseen entities, especially for company, organization and position categories. |
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. |
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition (2022.findings-naacl)
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| Challenge: | Named entity recognition (NER) is a system for identifying text spans pertaining to specific entity types. |
| Approach: | They propose a method to investigate the regularity of Chinese NER's entity mentions by a regularity-aware module and a periodicity-gnostic module. |
| Outcome: | The proposed model significantly outperforms previous state-of-the-art methods on three benchmark datasets and a practical medical dataset. |
Chinese NER Using Lattice LSTM (P18-1)
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| Challenge: | Chinese named entity recognition (NER) is a fundamental task in information extraction. |
| Approach: | They propose a lattice-structured LSTM model for Chinese named entity recognition (NER) model leverages word and word sequence information to encode a sequence of input characters and all potential words that match a lexicon. |
| Outcome: | The proposed model outperforms word-based and character-based models on Chinese NER datasets. |
Adaptive Threshold Selective Self-Attention for Chinese NER (2022.coling-1)
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| Challenge: | Named entity recognition (NER) is a computationally difficult task in Chinese since there is no natural delimiter between words in sentences. |
| Approach: | They propose a data-driven Adaptive Threshold Selective Self-Attention mechanism to select the most relevant characters to enhance Transformer architecture for Chinese named entity recognition. |
| Outcome: | Experiments on four benchmark Chinese NER datasets show the proposed mechanism improves performance. |
Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing studies focus on internal features of Chinese named entity recognition, but neglect other lingual modalities. |
| Approach: | They propose a bilingual enhancement module for Chinese Named Entity Recognition . they integrate rich English information into Chinese representation and use it to learn the interaction between bilinguals and dependent information within Chinese. |
| Outcome: | The proposed model can learn the interaction of bilinguals and dependent information within Chinese. |
A Large-Scale Chinese Multimodal NER Dataset with Speech Clues (2021.acl-long)
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| Challenge: | Using a large-scale dataset, we explore Chinese named entity recognition (NER) with both textual and acoustic contents. |
| Approach: | They propose a Chinese multimodal named entity recognition dataset . their corpus contains 42,987 annotated sentences and 71 hours of speech data . |
| Outcome: | The proposed model yields state-of-the-art (SoTA) results on Chinese multimodal named entity recognition (NER) based on 42,987 annotated sentences and 71 hours of speech data. |
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition (N19-1)
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| Challenge: | Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. |
| Approach: | They propose to use a Chinese Named Entity Recognition (NER) model that uses a character-based convolutional neural network and a gated recurrent unit to capture the information from adjacent characters and sentence contexts. |
| Outcome: | The proposed model outperforms existing models on Weibo, MSRA and Chinese Resume datasets. |
Entity Enhanced BERT Pre-training for Chinese NER (2020.emnlp-main)
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| Challenge: | Character-level BERT pre-trained in Chinese suffers from lacking lexicon information, which shows effectiveness for Chinese NER. |
| Approach: | They propose a semi-supervised method to integrate lexicon into pre-trained LMs in Chinese . they extract an entity lexiconal from raw text and integrate it into BERT . |
| Outcome: | The proposed method is highly effective and achieves the best results on a news dataset and two datasets annotated by the authors. |
FLAT: Chinese NER Using Flat-Lattice Transformer (2020.acl-main)
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| Challenge: | Named entity recognition (NER) models are difficult to use because of the complex nature of the lattice structure and the low inference speed. |
| Approach: | They propose a character-word lattice structure that converts lattics into flat structures consisting of spans. |
| Outcome: | The proposed model outperforms other lexicon-based models on four datasets and is highly parallel. |