NER-Overview-For-USTC

Table of Content

• Papers
  • Deep Learning Based Methods
  • Feature engineering-based Approaches
  • Transformer/PLM based Approach
  • LLM Based Methods
• Datasets

Papers

Deep Learning Based Methods

• Deep learning for efficient discriminative parsing
  Ronan Collobert
  CAIS,2021, [Paper]
  “deep” recurrent convolutional graph transformer network (GTN)
  Data: English Penn Treebank benchmark

• Using tf-idf to determine word relevance in document queries
  Juan Ramos
  ICML,2003,[Paper]
  Term Frequency Inverse Document Frequency (TF-IDF)
  Data: LDC's United Nations Parallel Text Corpus

• Distributed representations of words and phrases and their compositionality
  T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean
  Neurips,2013,[Paper]
  Word2Vec
  Data:

• Glove: Global vectors for word representation
  J Pennington, R Socher
  EMNLP,2014,[Paper]
  Glove
  Data:

• Enriching word vectors with subword information
  P Bojanowski, E Grave, A Joulin, T Mikolov
  arXiv,2016,[Paper]
  FastText
  Data:

• Improving language understanding by generative pre-training
  A Radford, K Narasimhan, T Salimans, I Sutskever
  Open AI,2018,[Paper]
  GPT
  Data:

• Natural language processing (almost) from scratch
  R Collobert, J Weston, L Bottou, M Karlen, K Kavukcuoglu, P Kuksa
  JLMR,2011,[Paper]
  No
  Data:

• Bidirectional lstm-crf models for sequence tagging
  Z Huang, W Xu, K Yu
  arXiv,2015,[Paper]
  BI-LSTM-CRF
  Data:

• End-to-end sequence labeling via bi-directional lstm-cnns-crf
  X Ma, E Hovy
  arXiv,2016,[Paper]
  No
  Data:

• Neural architectures for named entity recognition
  G Lample, M Ballesteros, S Subramanian, K Kawakami, C Dyer
  arXiv,2016,[Paper]
  No
  Data:

• Combining neural and knowledge-based approaches to named entity recognition in polish
  S Dadas
  AISC,2019,[Paper]
  No
  Data:

• Deep contextualized word representations
  Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer.
  Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,2018,[Paper]
  No
  Data:

• Semi-supervised sequence tagging with bidirectional language models
  Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power
  Annual Meeting of the Association for Computational Linguistics,2017,[Paper]
  No
  Data:

• CNN-Based Chinese NER with Lexicon Rethinking
  T Gui, R Ma, Q Zhang, L Zhao, YG Jiang, X Huang
  IJCAI,2019,[Paper]
  No
  Data:

• GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text
  Q Zhu, X Li, A Conesa, C Pereira
  Bioinformatics,,2018,[Paper]
  GRAM-CNN
  Data:

• An investigation of recurrent neural architectures for drug name recognition
  R Chalapathy, EZ Borzeshi, M Piccardi
  arXiv,2016,[Paper]
  No
  Data:

• Chinese NER using lattice LSTM
  Y Zhang, J Yang
  arXiv,2018,[Paper]
  No
  Data:

• An attention-based bilstm-crf approach to document-level chemical named entity recognition
  L Luo, Z Yang, P Yang, Y Zhang, L Wang, H Lin, J Wang
  Bioinformatics,2018,[Paper]
  No
  Data:

• Reliability-aware dynamic feature composition for name tagging
  Y Lin, L Liu, H Ji, D Yu, J Han
  ACL,2019,[Paper]
  No
  Data:

• Named entity recognition by neural sliding window
  I Gallo, E Binaghi, M Carullo, N Lamberti
  IAPR International Workshop on Document Analysis,2008,[Paper]
  No
  Data:

• Neural models for sequence chunking
  Feifei Zhai, Saloni Potdar, Bing Xiang, and Bowen Zhou.
  AAAI,2017,[Paper Download]
  No
  Data:

Feature engineering-based Approaches

Transformer/PLM based Approach

Large Language Model(LLM) based Approach

Datasets