Fetching the paper…
Reading the bibliography…
The synthesis process is essential for achieving computational experiment design in the field of inorganic materials chemistry.
Genia corpus - a semantically annotated corpus for bio-textmining
J.-D. Kim, T. Ohta, Y. Tateisi, and J. Tsujii · 2003
Earlier work this paper cites.
Boosting automatic event extraction from the literature using domain adaptation and coreference resolution
M. Miwa, P. Thompson, and S. Ananiadou · 2012
Earlier work this paper cites.
brat: a web-based tool for nlp-assisted text annotation
P. Stenetorp, S. Pyysalo, G. Topic, T. Ohta, S. Ananiadou, and J. Tsujii · 2012
Earlier work this paper cites.
Learning biological processes with global constraints
A. T. Scaria, J. Berant, M. Wang, P. Clark, J. Lewis, B. Harding, and C. D. Manning · 2013
Earlier work this paper cites.
Modeling biological processes for reading comprehension
J. Berant, V. Srikumar, P.-C. Chen, A. V. Linden, B. Harding, B. Huang, P. Clark, and C. D. Manning · 2014
Earlier work this paper cites.
Flow graph corpus from recipe texts
S. Mori, H. Maeta, Y. Yamakata, and T. Sasada · 2014
Earlier work this paper cites.
Lexical event ordering with an edge-factored model
O. Abend, S. B. Cohen, and M. Steedman · 2015
Earlier work this paper cites.
Bidirectional lstm-crf models for sequence tagging
Z. Huang, W. L. Xu, and K. Yu · 2015
Earlier work this paper cites.
Mise en place: Unsupervised interpretation of instructional recipes
C. Kiddon, G. T. Ponnuraj, L. S. Zettlemoyer, and Y. Choi · 2015
Earlier work this paper cites.
The chemdner corpus of chemicals and drugs and its annotation principles
M. Krallinger, O. Rabal, F. Leitner, M. Vazquez, and D. S. et al · 2015
Earlier work this paper cites.
A framework for procedural text understanding
H. Maeta, T. Sasada, and S. Mori · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
X. Zhang, J. J. Zhao, and Y. LeCun · 2015
Earlier work this paper cites.
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
A. Agrawal and A. Choudhary · 2016
Earlier work this paper cites.
Preparation and electrochemical properties of mg2+ and f- co-doped li4ti5o12 anode material for use in the lithium-ion batteries
X. Bai, W. Li, A. Wei, X. Li, L. Zhang, and Z. Liu · 2016
Cited alongside, same era.
Simpler context-dependent logical forms via model projections
R. Long, P. Pasupat, and P. Liang · 2016
Cited alongside, same era.
Neural machine translation of rare words with subword units
R. Sennrich, B. Haddow, and A. Birch · 2016
Cited alongside, same era.
Machine-learned and codified synthesis parameters of oxide materials
E. Y. Kim, K. Huang, A. Tomala, S. L. Matthews, E. Strubell, A. R. Saunders, A. L. McCallum, and E. Olivetti · 2017
Cited alongside, same era.
Automatically extracting action graphs from materials science synthesis procedures
S. Mysore, E. Kim, E. Strubell, A. Liu, H.-S. Chang, S. Kompella, K. Huang, A. McCallum, and E. Olivetti · 2017
Cited alongside, same era.
Deep contextualized word representations
M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer · 2018
Later among the works it cites.
Chemical compounds knowledge visualization with natural language processing and linked data
K. Tanaka, T. Iwakura, Y. Koyanagi, N. Ikeda, H. Shindo, and Y. Matsumoto · 2018
Later among the works it cites.
Flair: An easy-to-use framework for state-of-the-art nlp
A. Akbik, T. Bergmann, D. Blythe, K. Rasul, S. Schweter, and R. Vollgraf · 2019
Later among the works it cites.
Scibert: A pretrained language model for scientific text
I. Beltagy, K. Lo, and A. Cohan · 2019
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Later among the works it cites.
Distilling a materials synthesis ontology
E. Kim, K. Huang, O. Kononova, G. Ceder, and E. Olivetti · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Biomedical event trigger identification using bidirectional recurrent neural network based models
P. V. S. S. Rahul, S. K. Sahu, and A. Anand · 2017
Cited alongside, same era.
Biomedical event extraction using abstract meaning representation
S. Rao, D. Marcu, K. Knight, and H. Daumé · 2017
Cited alongside, same era.
Protein fold recognition with representation learning and long short-term memory
M. Tsubaki, M. Shimbo, and Y. Matsumoto · 2017
Cited alongside, same era.
Biomedical event extraction using convolutional neural networks and dependency parsing
J. Björne and T. Salakoski · 2018
Cited alongside, same era.
Machine learning for molecular and materials science
K. T. Butler, D. W. Davies, H. M. Cartwright, O. Isayev, and A. Walsh · 2018
Cited alongside, same era.
Tracking state changes in procedural text: a challenge dataset and models for process paragraph comprehension
B. Dalvi, L. Huang, N. Tandon, W. tau Yih, and P. E. Clark · 2018
Cited alongside, same era.
Automatic extraction of emergency response process models from chinese plans
W. Guo, Q. Zeng, H. Duan, G. Yuan, W. Ni, and C. Liu · 2018
Cited alongside, same era.
Later among the works it cites.
Text-mined dataset of inorganic materials synthesis recipes
O. Kononova, H. Huo, T. He, Z. Rong, T. Botari, W. Sun, V. Tshitoyan, and G. Ceder · 2019
Later among the works it cites.
The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures
S. Mysore, Z. Jensen, E. Kim, K. Huang, H.-S. Chang, E. Strubell, J. Flanigan, A. McCallum, and E. Olivetti · 2019
Later among the works it cites.
Scispacy: Fast and robust models for biomedical natural language processing
M. Neumann, D. King, I. Beltagy, and W. Ammar · 2019
Later among the works it cites.
Playing by the book: An interactive game approach for action graph extraction from text
R. Tamari, H. Shindo, D. Shahaf, and Y. Matsumoto · 2019
Later among the works it cites.
Unsupervised word embeddings capture latent knowledge from materials science literature
V. Tshitoyan, J. Dagdelen, L. Weston, A. Dunn, Z. Rong, O. Kononova, K. A. Persson, G. Ceder, and A. Jain · 2019
Later among the works it cites.
Machine learning in materials science
J. Wei, X. Chu, X.-Y. Sun, K. Xu, H.-X. Deng, J. Chen, Z. Wei, and M. Lei · 2019
Later among the works it cites.