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Information Extraction refers to a collection of tasks within Natural Language Processing (NLP) that identifies sub-sequences within text and their labels.
Transporting the linguistic string project system from a medical to a navy domain
E. Marsh and C. Friedman · 1985
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MUC-5 evaluation metrics
N. Chinchor and B. Sundheim · 1993
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Building a large annotated corpus of english: The penn treebank
M. Marcus, B. Santorini, and M. A. Marcinkiewicz · 1993
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Convolutional networks for images, speech, and time series
Y. LeCun, Y. Bengio, et al · 1995
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Information extraction
J. Cowie and W. Lehnert · 1996
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Message Understanding Conference- 6: A brief history
R. Grishman and B. Sundheim · 1996
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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An introduction to latent semantic analysis
T. K. Landauer, P. W. Foltz, and D. Laham · 1998
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Maximum entropy markov models for information extraction and segmentation
A. McCallum, D. Freitag, and F. C. Pereira · 2000
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
J. D. Lafferty, A. McCallum, and F. C. N. Pereira · 2001
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An information-theoretic perspective of tf–idf measures
A. Aizawa · 2003
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
E. F. Tjong Kim Sang and F. De Meulder · 2003
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NLTK: The natural language toolkit
S. Bird and E. Loper · 2004
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The automatic content extraction (ACE) program – tasks, data, and evaluation
G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, and R. Weischedel · 2004
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Analyzing grammar: An introduction
P. R. Kroeger · 2005
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Understanding source code evolution using abstract syntax tree matching
I. Neamtiu, J. S. Foster, and M. Hicks · 2005
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Syntactic parsing
M. J. Pickering and R. P. Van Gompel · 2006
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Word classes
J. Rijkhoff · 2007
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Tagme: On-the-fly annotation of short text fragments (by wikipedia entities)
P. Ferragina and U. Scaiella · 2010
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Robust disambiguation of named entities in text
J. Hoffart, M. A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol, B. Taneva, S. Thater, and G. Weikum · 2011
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Named entity recognition using hidden markov model (hmm)
S. Morwal, N. Jahan, and D. Chopra · 2012
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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A fast and accurate dependency parser using neural networks
D. Chen and C. Manning · 2014
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The Stanford CoreNLP natural language processing toolkit
C. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. Bethard, and D. McClosky · 2014
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GloVe: Global vectors for word representation
J. Pennington, R. Socher, and C. Manning · 2014
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Entity linking with a knowledge base: Issues, techniques, and solutions
W. Shen, J. Wang, and J. Han · 2014
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FINET: Context-aware fine-grained named entity typing
L. Del Corro, A. Abujabal, R. Gemulla, and G. Weikum · 2015
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Bidirectional lstm-crf models for sequence tagging
Z. Huang, W. Xu, and K. Yu · 2015
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Pointer networks
O. Vinyals, M. Fortunato, and N. Jaitly · 2015
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Text understanding with the attention sum reader network
R. Kadlec, M. Schmid, O. Bajgar, and J. Kleindienst · 2016
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End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
X. Ma and E. Hovy · 2016
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Label embedding for zero-shot fine-grained named entity typing
Y. Ma, E. Cambria, and S. Gao · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang · 2016
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Iterative alternating neural attention for machine reading
A. Sordoni, P. Bachman, A. Trischler, and Y. Bengio · 2016
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Machine comprehension using match-lstm and answer pointer
S. Wang and J. Jiang · 2016
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Joint learning of the embedding of words and entities for named entity disambiguation
I. Yamada, H. Shindo, H. Takeda, and Y. Takefuji · 2016
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End-to-end answer chunk extraction and ranking for reading comprehension
Y. Yu, W. Zhang, K. Hasan, M. Yu, B. Xiang, and B. Zhou · 2016
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Enriching Word Vectors with Subword Information
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov · 2017
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Gated-attention readers for text comprehension
B. Dhingra, H. Liu, Z. Yang, W. Cohen, and R. Salakhutdinov · 2017
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Deep joint entity disambiguation with local neural attention
O.-E. Ganea and T. Hofmann · 2017
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spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing
M. Honnibal and I. Montani · 2017
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Leveraging linguistic structures for named entity recognition with bidirectional recursive neural networks
P.-H. Li, R.-P. Dong, Y.-S. Wang, J.-C. Chou, and W.-Y. Ma · 2017
Cited alongside, same era.
Neupl: Attention-based semantic matching and pair-linking for entity disambiguation
M. C. Phan, A. Sun, Y. Tay, J. Han, and C. Li · 2017
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
M. Seo, A. Kembhavi, A. Farhadi, and H. Hajishirzi · 2017
Cited alongside, same era.
Fast and accurate entity recognition with iterated dilated convolutions
E. Strubell, P. Verga, D. Belanger, and A. McCallum · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Neural models for sequence chunking
F. Zhai, S. Potdar, B. Xiang, and B. Zhou · 2017
Learning to extract attribute value from product via question answering: A multi-task approach
Q. Wang, L. Yang, B. Kanagal, S. Sanghai, D. Sivakumar, B. Shu, Z. Yu, and J. Elsas · 2020
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Scalable zero-shot entity linking with dense entity retrieval
L. Wu, F. Petroni, M. Josifoski, S. Riedel, and L. Zettlemoyer · 2020
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Wikipedia2Vec: An efficient toolkit for learning and visualizing the embeddings of words and entities from Wikipedia
I. Yamada, A. Asai, J. Sakuma, H. Shindo, H. Takeda, Y. Takefuji, and Y. Matsumoto · 2020
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LUKE: Deep contextualized entity representations with entity-aware self-attention
I. Yamada, A. Asai, H. Shindo, H. Takeda, and Y. Matsumoto · 2020
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Tri-train: Automatic pre-fine tuning between pre-training and fine-tuning for SciNER
Q. Zeng, W. Yu, M. Yu, T. Jiang, T. Weninger, and M. Jiang · 2020
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Cited alongside, same era.
Ultra-fine entity typing
E. Choi, O. Levy, Y. Choi, and L. Zettlemoyer · 2018
Cited alongside, same era.
End-to-end neural entity linking
N. Kolitsas, O.-E. Ganea, and T. Hofmann · 2018
Cited alongside, same era.
Improving entity linking by modeling latent relations between mentions
P. Le and I. Titov · 2018
Cited alongside, same era.
Hierarchical losses and new resources for fine-grained entity typing and linking
S. Murty, P. Verga, L. Vilnis, I. Radovanovic, and A. McCallum · 2018
Cited alongside, same era.
Pair-linking for collective entity disambiguation: Two could be better than all
M. C. Phan, A. Sun, Y. Tay, J. Han, and C. Li · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, et al · 2018
Cited alongside, same era.
Clustering-based inference for biomedical entity linking
R. Angell, N. Monath, S. Mohan, N. Yadav, and A. McCallum · 2021
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Autoregressive entity retrieval
N. D. Cao, G. Izacard, S. Riedel, and F. Petroni · 2021
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Prompt-learning for fine-grained entity typing
N. Ding, Y. Chen, X. Han, G. Xu, P. Xie, H.-T. Zheng, Z. Liu, J. Li, and H.-G. Kim · 2021
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Read, retrospect, select: An mrc framework to short text entity linking
Y. Gu, X. Qu, Z. Wang, B. Huai, N. J. Yuan, and X. Gui · 2021
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Few-shot named entity recognition: An empirical baseline study
J. Huang, C. Li, K. Subudhi, D. Jose, S. Balakrishnan, W. Chen, B. Peng, J. Gao, and J. Han · 2021
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Pretrained language models for text generation: A survey
J. Li, T. Tang, W. X. Zhao, and J.-R. Wen · 2021
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Empirical analysis of unlabeled entity problem in named entity recognition
Y. Li, lemao liu, and S. Shi · 2021
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Entity linking meets deep learning: Techniques and solutions
W. Shen, Y. Li, Y. Liu, J. Han, J. Wang, and X. Yuan · 2021
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Locate and label: A two-stage identifier for nested named entity recognition
Y. Shen, X. Ma, Z. Tan, S. Zhang, W. Wang, and W. Lu · 2021
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ChineseBERT: Chinese pretraining enhanced by glyph and Pinyin information
Z. Sun, X. Li, X. Sun, Y. Meng, X. Ao, Q. He, F. Wu, and J. Li · 2021
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ChemNER: Fine-grained chemistry named entity recognition with ontology-guided distant supervision
X. Wang, V. Hu, X. Song, S. Garg, J. Xiao, and J. Han · 2021
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A unified generative framework for various NER subtasks
H. Yan, T. Gui, J. Dai, Q. Guo, Z. Zhang, and X. Qiu · 2021
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AdaTag: Multi-attribute value extraction from product profiles with adaptive decoding
J. Yan, N. Zalmout, Y. Liang, C. Grant, X. Ren, and X. L. Dong · 2021
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Retrospective reader for machine reading comprehension
Z. Zhang, J. Yang, and H. Zhao · 2021
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A frustratingly easy approach for entity and relation extraction
Z. Zhong and D. Chen · 2021
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Learning from noisy labels for entity-centric information extraction
W. Zhou and M. Chen · 2021
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Entity linking via explicit mention-mention coreference modeling
D. Agarwal, R. Angell, N. Monath, and A. McCallum · 2022
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Improving entity disambiguation by reasoning over a knowledge base
T. Ayoola, J. Fisher, and A. Pierleoni · 2022
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ReFinED: An efficient zero-shot-capable approach to end-to-end entity linking
T. Ayoola, S. Tyagi, J. Fisher, C. Christodoulopoulos, and A. Pierleoni · 2022
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ExtEnD: Extractive entity disambiguation
E. Barba, L. Procopio, and R. Navigli · 2022
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Posthoc verification and the fallibility of the ground truth
Y. Ding, N. Botzer, and T. Weninger · 2022
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Ask-and-verify: Span candidate generation and verification for attribute value extraction
Y. Ding, Y. Liang, N. Zalmout, X. Li, C. Grant, and T. Weninger · 2022
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LasUIE: Unifying information extraction with latent adaptive structure-aware generative language model
H. Fei, S. Wu, J. Li, B. Li, F. Li, L. Qin, M. Zhang, M. Zhang, and T.-S. Chua · 2022
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Unified named entity recognition as word-word relation classification
J. Li, H. Fei, J. Liu, S. Wu, M. Zhang, C. Teng, D. Ji, and F. Li · 2022
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A survey on deep learning for named entity recognition
J. Li, A. Sun, J. Han, and C. Li · 2022
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Unified structure generation for universal information extraction
Y. Lu, Q. Liu, D. Dai, X. Xiao, H. Lin, X. Han, L. Sun, and H. Wu · 2022
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Deeptype 2: Superhuman entity linking, all you need is type interactions
J. Raiman · 2022
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Neural entity linking: A survey of models based on deep learning
Ö. Sevgili, A. Shelmanov, M. Arkhipov, A. Panchenko, and C. Biemann · 2022
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Wikipedia: Complete guide — history, products, founding, and more, 2022
H. C. Staff · 2022
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Z. Wang, K. Zhao, Z. Wang, and J. Shang · 2022
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Global entity disambiguation with BERT
I. Yamada, K. Washio, H. Shindo, and Y. Matsumoto · 2022
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Generate rather than retrieve: Large language models are strong context generators
W. Yu, D. Iter, S. Wang, Y. Xu, M. Ju, S. Sanyal, C. Zhu, M. Zeng, and M. Jiang · 2022
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Prototype-representations for training data filtering in weakly-supervised information extraction
N. Zalmout and X. Li · 2022
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EntQA: Entity linking as question answering
W. Zhang, W. Hua, and K. Stratos · 2022
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig · 2023
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