Improving Chinese Named Entity Recognition by Search Engine Augmentation

• URL: http://arxiv.org/abs/2210.12662v1
• Date: Sun, 23 Oct 2022 08:42:05 GMT
• Title: Improving Chinese Named Entity Recognition by Search Engine Augmentation
• Authors: Qinghua Mao and Jiatong Li and Kui Meng
• Abstract summary: We propose a neural-based approach to perform semantic augmentation using external knowledge from search engine for Chinese NER. In particular, a multi-channel semantic fusion model is adopted to generate the augmented input representations, which aggregates external related texts retrieved from the search engine.
• Score: 2.971423962840551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared with English, Chinese suffers from more grammatical ambiguities, like fuzzy word boundaries and polysemous words. In this case, contextual information is not sufficient to support Chinese named entity recognition (NER), especially for rare and emerging named entities. Semantic augmentation using external knowledge is a potential way to alleviate this problem, while how to obtain and leverage external knowledge for the NER task remains a challenge. In this paper, we propose a neural-based approach to perform semantic augmentation using external knowledge from search engine for Chinese NER. In particular, a multi-channel semantic fusion model is adopted to generate the augmented input representations, which aggregates external related texts retrieved from the search engine. Experiments have shown the superiority of our model across 4 NER datasets, including formal and social media language contexts, which further prove the effectiveness of our approach.

Related papers

• DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition [94.90258603217008]
  The MultiCoNER RNum2 shared task aims to tackle multilingual named entity recognition (NER) in fine-grained and noisy scenarios. Previous top systems in the MultiCoNER RNum1 either incorporate the knowledge bases or gazetteers. We propose a unified retrieval-augmented system (U-RaNER) for fine-grained multilingual NER.
  arXiv  Detail & Related papers  (2023-05-05T16:59:26Z)
• IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases [53.054598423181844]
  We present a novel NER cascade approach comprising three steps. We empirically demonstrate the significance of external knowledge bases in accurately classifying fine-grained and emerging entities. Our system exhibits robust performance in the MultiCoNER2 shared task, even in the low-resource language setting.
  arXiv  Detail & Related papers  (2023-04-20T20:30:34Z)
• 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)
• DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition [94.1865071914727]
  MultiCoNER aims at detecting semantically ambiguous named entities in short and low-context settings for multiple languages. Our team DAMO-NLP proposes a knowledge-based system, where we build a multilingual knowledge base based on Wikipedia. Given an input sentence, our system effectively retrieves related contexts from the knowledge base. Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
  arXiv  Detail & Related papers  (2022-03-01T15:29:35Z)
• TURNER: The Uncertainty-based Retrieval Framework for Chinese NER [46.063487367225754]
  We propose TURNER: The Uncertainty-based Retrieval framework for Chinese NER. The idea behind TURNER is to imitate human behavior: we frequently retrieve auxiliary knowledge as assistance when encountering an unknown or uncertain entity. Experiments on four benchmark datasets demonstrate TURNER's effectiveness.
  arXiv  Detail & Related papers  (2022-02-18T05:05:22Z)
• Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis [96.53859361560505]
  We propose a knowledge graph augmented network (KGAN) to incorporate external knowledge with explicitly syntactic and contextual information. KGAN captures the sentiment feature representations from multiple perspectives, i.e., context-, syntax- and knowledge-based. Experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN.
  arXiv  Detail & Related papers  (2022-01-13T08:25:53Z)
• Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding [15.955385058787348]
  We develop a distribution-aware word embedding and implement three different methods to make use of the distribution information in a NER framework. The performance of NER will be improved if the word specificity is incorporated into existing NER methods.
  arXiv  Detail & Related papers  (2021-09-03T17:28:04Z)
• Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition [54.92161571089808]
  Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources. Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages. We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-01T05:46:22Z)
• Exhaustive Entity Recognition for Coptic: Challenges and Solutions [8.980876474818153]
  In this paper we present entity recognition for Coptic, the language of Hellenistic era Egypt. Weevaluate NLP approaches to the task and lay out difficulties in applying them to a low-resource,morphologically complex language. We present solutions for named and non-named nested en-tity recognition and semi-automatic entity linking to Wikipedia, relying on robust dependencyparsing, feature-based CRF models, and hand-crafted knowledge base resources.
  arXiv  Detail & Related papers  (2020-11-03T23:49:42Z)
• Integrating Boundary Assembling into a DNN Framework for Named Entity Recognition in Chinese Social Media Text [3.7239227834407735]
  Chinese word boundaries are also entity boundaries, so named entity recognition for Chinese text can benefit from word boundary detection. In this paper, we integrate a boundary assembling method with the state-of-the-art deep neural network model, and incorporate the updated word boundary information into a conditional random field model for named entity recognition. Our method shows a 2% absolute improvement over previous state-of-the-art results.
  arXiv  Detail & Related papers  (2020-02-27T04:29:13Z)

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.