命名實體識別(NER)(也稱為實體標識,實體組塊和實體提取)是信息抽取的子任務,旨在將非結構化文本中提到的命名實體定位和分類為預定義類別,例如人員姓名、地名、機構名、專有名詞等。

知識薈萃

命名實體識別 Named Entity Recognition 專知薈萃

綜述

  1. Jing Li, Aixin Sun,Jianglei Han, Chenliang Li

  2. A Review of Named Entity Recognition (NER) Using Automatic Summarization of Resumes

模型算法

  1. LSTM + CRF中的NCRF++算法:Design Challenges and Misconceptions in Neural Sequence Labeling.COLLING 2018.

  2. CNN+CRF:

  3. BERT+(LSTM)+CRF:

入門學習

  1. NLP之CRF應用篇(序列標注任務)( CRF++的詳細解析、Bi-LSTM+CRF中CRF層的詳細解析、Bi-LSTM後加CRF的原因、CRF和Bi-LSTM+CRF優化目標的區別) )

  2. Bilstm+CRF中的CRF詳解

  3. Bilstm-CRF中的CRF層解析-2

  4. Bilstm-CRF中的CRF層解析-3

  5. CRF和LSTM模型在序列標注上的優劣?

  6. CRF和LSTM的比較

  7. 入門參考:命名實體識別(NER)的二三事

  8. 基礎卻不簡單,命名實體識別的難點與現狀

  9. 通俗理解BiLSTM-CRF命名實體識別模型中的CRF層

重要報告

Tutorial

1.(pyToech)高級:製定動態決策和BI-LSTM CRF(Advanced: Making Dynamic Decisions and the Bi-LSTM CRF) - [https://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html]

代碼

1.中文命名實體識別(包括多種模型:HMM,CRF,BiLSTM,BiLSTM+CRF的具體實現)

- [https://github.com/luopeixiang/named_entity_recognition]

領域專家

1.華為-諾亞方舟 - 李航 []

2.美國伊利諾伊大學 - 韓家煒 [https://hanj.cs.illinois.edu/]

命名實體識別工具

  1. Stanford NER
  2. MALLET
  3. Hanlp
  4. NLTK
  5. spaCy
  6. Ohio State University Twitter NER

###相關數據集

  1. CCKS2017 開放的中文的電子病例測評相關的數據。 評測任務一:

  2. CCKS2018 開放的音樂領域的實體識別任務。

評測任務:

- [https://biendata.com/competition/CCKS2018_2/]
  1. NLPCC2018 開放的任務型對話係統中的口語理解評測。

CoNLL 2003

https://www.clips.uantwerpen.be/conll2003/ner/

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VIP內容

命名實體識別(Named Entity Recognition,NER)作為自然語言處理領域經典的研究主題,是智能問答、知識圖譜等任務的基礎技術。領域命名實體識別(Domain Named Entity Recognition,DNER)是麵向特定領域的NER方案。在深度學習技術的推動下,中文DNER取得了突破性進展。概括了中文DNER的研究框架,從領域數據源的確定、領域實體類型及規範製定、領域數據集的標注規範、中文DNER評估指標四個角度對國內外已有研究成果進行了綜合評述;總結了目前常見的中文DNER的技術框架,介紹了基於詞典和規則的模式匹配方法、統計機器學習方法、基於深度學習的方法、多方融合的深度學習方法,並重點分析了基於詞向量表征和深度學習的中文DNER方法;討論了中文DNER的典型應用場景,對未來發展方向進行了展望。

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This paper describes the system developed by the USTC-NELSLIP team for SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition (MultiCoNER). We propose a gazetteer-adapted integration network (GAIN) to improve the performance of language models for recognizing complex named entities. The method first adapts the representations of gazetteer networks to those of language models by minimizing the KL divergence between them. After adaptation, these two networks are then integrated for backend supervised named entity recognition (NER) training. The proposed method is applied to several state-of-the-art Transformer-based NER models with a gazetteer built from Wikidata, and shows great generalization ability across them. The final predictions are derived from an ensemble of these trained models. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on three tracks (Chinese, Code-mixed and Bangla) and 2nd on the other ten tracks in this task.

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