FGN: Fusion Glyph Network for Chinese Named Entity Recognition

• URL: http://arxiv.org/abs/2001.05272v6
• Date: Thu, 8 Oct 2020 11:46:09 GMT
• Title: FGN: Fusion Glyph Network for Chinese Named Entity Recognition
• Authors: Zhenyu Xuan, Rui Bao, Shengyi Jiang
• Abstract summary: We propose the FGN, Fusion Glyph Network for Chinese NER. FGN captures both glyph information and interactive information between glyphs from neighboring characters. Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
• Score: 5.653869962555973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chinese NER is a challenging task. As pictographs, Chinese characters contain latent glyph information, which is often overlooked. In this paper, we propose the FGN, Fusion Glyph Network for Chinese NER. Except for adding glyph information, this method may also add extra interactive information with the fusion mechanism. The major innovations of FGN include: (1) a novel CNN structure called CGS-CNN is proposed to capture both glyph information and interactive information between glyphs from neighboring characters. (2) we provide a method with sliding window and Slice-Attention to fuse the BERT representation and glyph representation for a character, which may capture potential interactive knowledge between context and glyph. Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER. Further, more experiments are conducted to investigate the influences of various components and settings in FGN.

Related papers

• Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models [58.952782707682815]
  COFT is a novel method to focus on different-level key texts, thereby avoiding getting lost in lengthy contexts. Experiments on the knowledge hallucination benchmark demonstrate the effectiveness of COFT, leading to a superior performance over $30%$ in the F1 score metric.
  arXiv  Detail & Related papers  (2024-10-19T13:59:48Z)
• KNN Transformer with Pyramid Prompts for Few-Shot Learning [52.735070934075736]
  Few-Shot Learning aims to recognize new classes with limited labeled data. Recent studies have attempted to address the challenge of rare samples with textual prompts to modulate visual features.
  arXiv  Detail & Related papers  (2024-10-14T07:39:30Z)
• PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud [54.046884854230555]
  This paper introduces PAI-Diffusion, a comprehensive framework for Chinese text-to-image synthesis. It incorporates both general and domain-specific Chinese diffusion models, enabling the generation of contextually relevant images. It seamlessly integrates with Alibaba Cloud's Machine Learning Platform for AI, providing accessible and scalable solutions.
  arXiv  Detail & Related papers  (2023-09-11T15:18:28Z)
• Chinese Financial Text Emotion Mining: GCGTS -- A Character Relationship-based Approach for Simultaneous Aspect-Opinion Pair Extraction [7.484918031250864]
  Aspect-Opinion Pair Extraction (AOPE) from Chinese financial texts is a specialized task in fine-grained text sentiment analysis. Previous studies have mainly focused on developing grid annotation schemes within grid-based models to facilitate this extraction process. We propose a novel method called Graph-based Character-level Grid Tagging Scheme (GCGTS) The GCGTS method explicitly incorporates syntactic structure using Graph Convolutional Networks (GCN) and unifies the encoding of characters within the same semantic unit (Chinese word level)
  arXiv  Detail & Related papers  (2023-08-04T02:20:56Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)
• Dynamic Multi-View Fusion Mechanism For Chinese Relation Extraction [12.818297160055584]
  We propose a mixture-of-view-experts framework (MoVE) to dynamically learn multi-view features for Chinese relation extraction. With both the internal and external knowledge of Chinese characters, our framework can better capture the semantic information of Chinese characters.
  arXiv  Detail & Related papers  (2023-03-09T07:35:31Z)
• MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition [21.190288516462704]
  This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER. Specifically, we use multi-metadata embedding in a two-stream Transformer to integrate Chinese character features with the radical-level embedding. With the structural characteristics of Chinese characters, MECT can better capture the semantic information of Chinese characters for NER.
  arXiv  Detail & Related papers  (2021-07-12T13:39:06Z)
• GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph [31.723483415041347]
  Previous works indicate that the glyph of Chinese characters contains rich semantic information. We propose a Chinese pre-trained representation model named as Glyph CRM. It abandons the ID-based character embedding method yet solely based on sequential character images.
  arXiv  Detail & Related papers  (2021-07-01T12:14:05Z)
• Recurrent neural network transducer for Japanese and Chinese offline handwritten text recognition [5.704448607986111]
  We propose an RNN-Transducer model for recognizing Japanese and Chinese offline handwritten text line images. The proposed model takes advantage of both visual and linguistic information from the input image. Experimental results show that the proposed model achieves state-of-the-art performance on all datasets.
  arXiv  Detail & Related papers  (2021-06-28T08:16:44Z)
• PIN: A Novel Parallel Interactive Network for Spoken Language Understanding [68.53121591998483]
  In the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them. The experiments on two benchmark datasets, i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach. More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.
  arXiv  Detail & Related papers  (2020-09-28T15:59:31Z)
• Global-local Enhancement Network for NMFs-aware Sign Language Recognition [135.30357113518127]
  We propose a simple yet effective architecture called Global-local Enhancement Network (GLE-Net) Of the two streams, one captures the global contextual relationship, while the other stream captures the discriminative fine-grained cues. We introduce the first non-manual-features-aware isolated Chinese sign language dataset with a total vocabulary size of 1,067 sign words in daily life.
  arXiv  Detail & Related papers  (2020-08-24T13:28:55Z)

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.