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Chemical patents are an important resource for chemical information.
Class-based n-gram models of natural language
Peter F Brown, Peter V Desouza, Robert L Mercer, Vincent J Della Pietra, and Jenifer C Lai. 1992 · 1992
Earlier work this paper cites.
Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal. 1997 · 1997
Earlier work this paper cites.
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001 · 2001
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Opennlp: A java-based nlp toolkit
Thomas Morton, Joern Kottmann, Jason Baldridge, and Gann Bierner. 2005 · 2005
Earlier work this paper cites.
Chebi: a database and ontology for chemical entities of biological interest
Kirill Degtyarenko, Paula De Matos, Marcus Ennis, Janna Hastings, Martin Zbinden, Alan McNaught, Rafael Alcántara, Michael Darsow, Mickaël Guedj, and Michael Ashburner. 2007 · 2007
Earlier work this paper cites.
Crfsuite: a fast implementation of conditional random fields
Naoaki Okazaki. 2007 · 2007
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Detection of iupac and iupac-like chemical names
Roman Klinger, Corinna Kolářik, Juliane Fluck, Martin Hofmann-Apitius, and Christoph M Friedrich. 2008 · 2008
Earlier work this paper cites.
The structural and content aspects of abstracts versus bodies of full text journal articles are different
K. Bretonnel Cohen, Helen L. Johnson, Karin Verspoor, Christophe Roeder, and Lawrence E. Hunter. 2010 · 2010
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Quantifying the challenges in parsing patent claims
S Verberne, EKL D’hondt, NHJ Oostdijk, and CHA Koster. 2010 · 2010
Earlier work this paper cites.
OSCAR4: a flexible architecture for chemical text-mining
David M Jessop, Sam E Adams, Egon L Willighagen, Lezan Hawizy, and Peter Murray-Rust. 2011 · 2011
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Current challenges in patent information retrieval , volume 29
Mihai Lupu, Katja Mayer, John Tait, and Anthony J Trippe. 2011 · 2011
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Making every sar point count: the development of chemistry connect for the large-scale integration of structure and bioactivity data
Sorel Muresan, Plamen Petrov, Christopher Southan, Magnus J Kjellberg, Thierry Kogej, Christian Tyrchan, Peter Varkonyi, and Paul Hongxing Xie. 2011 · 2011
Earlier work this paper cites.
Chemspot: a hybrid system for chemical named entity recognition
Tim Rocktäschel, Michael Weidlich, and Ulf Leser. 2012 · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Distributional semantics resources for biomedical text processing
Sampo Pyysalo, Filip Ginter, Hans Moen, Tapio Salakoski, and Sophia Ananiadou. 2013 · 2013
Earlier work this paper cites.
Annotated chemical patent corpus: a gold standard for text mining
Saber A Akhondi, Alexander G Klenner, Christian Tyrchan, Anil K Manchala, Kiran Boppana, Daniel Lowe, Marc Zimmermann, Sarma ARP Jagarlapudi, Roger Sayle, Jan A Kors, et al. 2014 · 2014
Cited alongside, same era.
Revisiting embedding features for simple semi-supervised learning
Jiang Guo, Wanxiang Che, Haifeng Wang, and Ting Liu. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
Bidirectional LSTM-CRF models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2015 · 2015
Cited alongside, same era.
Overview of the CHEMDNER patents task
Martin Krallinger, Obdulia Rabal, Analia Lourenço, Martin Perez Perez, Gael Perez Rodriguez, Miguel Vazquez, Florian Leitner, Julen Oyarzabal, and Alfonso Valencia. 2015 · 2015
Cited alongside, same era.
Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning
Yaoyun Zhang, Jun Xu, Hui Chen, Jingqi Wang, Yonghui Wu, Manu Prakasam, and Hua Xu. 2016 · 2016
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Allennlp: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 2017 · 2017
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Deep learning with word embeddings improves biomedical named entity recognition
Maryam Habibi, Leon Weber, Mariana Neves, David Luis Wiegandt, and Ulf Leser. 2017 · 2017
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Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
Nils Reimers and Iryna Gurevych. 2017b · 2017
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Patents and scientific papers: Quite different concepts: The reward is found in giving, not in keeping [retrospectroscope]
alphaXiv searches the wider corpus for related work and actual follow-ups.
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tmChem: a high performance approach for chemical named entity recognition and normalization
Robert Leaman, Chih-Hsuan Wei, and Zhiyong Lu. 2015 · 2015
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Cited alongside, same era.
Managing expectations: assessment of chemistry databases generated by automated extraction of chemical structures from patents
Stefan Senger, Luca Bartek, George Papadatos, and Anna Gaulton. 2015 · 2015
Cited alongside, same era.
Chemical entity recognition in patents by combining dictionary-based and statistical approaches
Saber A Akhondi, Ewoud Pons, Zubair Afzal, Herman van Haagen, Benedikt FH Becker, Kristina M Hettne, Erik M van Mulligen, and Jan A Kors. 2016 · 2016
Cited alongside, same era.
Chemtok: a new rule based tokenizer for chemical named entity recognition
Abbas Akkasi, Ekrem Varoğlu, and Nazife Dimililer. 2016 · 2016
Cited alongside, same era.
Recognizing chemicals in patents: a comparative analysis
Maryam Habibi, David Luis Wiegandt, Florian Schmedding, and Ulf Leser. 2016 · 2016
Cited alongside, same era.
Improving automated patent claim parsing: Dataset, system, and experiments
Mengke Hu, David Cinciruk, and John MacLaren Walsh. 2016 · 2016
Cited alongside, same era.
Max E Valentinuzzi. 2017 · 2017
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Chemlistem: chemical named entity recognition using recurrent neural networks
Peter Corbett and John Boyle. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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“found in translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
Philippe Schwaller, Theophile Gaudin, David Lanyi, Costas Bekas, and Teodoro Laino. 2018 · 2018
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Planning chemical syntheses with deep neural networks and symbolic ai
Marwin HS Segler, Mike Preuss, and Mark P Waller. 2018 · 2018
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Comparing cnn and lstm character-level embeddings in bilstm-crf models for chemical and disease named entity recognition
Zenan Zhai, Dat Quoc Nguyen, and Karin Verspoor. 2018 · 2018
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Automatic identification of relevant chemical compounds from patents
Saber A. Akhondi, Hinnerk Rey, Markus Schwörer, Michael Maier, John P. Toomey, Heike Nau, Gabriele Ilchmann, Mark Sheehan, Matthias Irmer, Claudia Bobach, Marius A. Doornenbal, Michelle Gregory, and Jan A. Kors. 2019 · 2019
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Using Similarity Measures to Select Pretraining Data for NER
Xiang Dai, Sarvnaz Karimi, Ben Hachey, and Cecile Paris. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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