Fetching the paper…
Reading the bibliography…
Neural networks have a remarkable capacity for contextual processing--using recent or nearby inputs to modify processing of current input.
A Comprehensive Grammar of the English Language
Quirk, R., Greenbaum, S., Leech, G., and Svartvik, J · 1985
Earlier work this paper cites.
A natural history of negation
Horn, L · 1989
Earlier work this paper cites.
Finding structure in time
Elman, J. L · 1990
Earlier work this paper cites.
Distributed representations, simple recurrent networks, and grammatical structure
Elman, J. L · 1991
Earlier work this paper cites.
Recurrent network model of the neural mechanism of short-term active memory
Zipser, D · 1991
Earlier work this paper cites.
Phase-space learning
Tsung, F.-S. and Cottrell, G. W · 1995
Earlier work this paper cites.
The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction
Casey, M · 1996
Earlier work this paper cites.
How the brain keeps the eyes still
Seung, H. S · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
A recurrent neural network that learns to count
Rodriguez, P., Wiles, J., and Elman, J. L · 1999
Earlier work this paper cites.
Negation and polarity: Syntactic and semantic perspectives
Horn, L. R. and Kato, Y · 2000
Earlier work this paper cites.
A simple algorithm for identifying negated findings and diseases in discharge summaries
Chapman, W. W., Bridewell, W., Hanbury, P., Cooper, G. F., and Buchanan, B. G · 2001
Earlier work this paper cites.
Nonlinear Systems
Khalil, H. K · 2001
Earlier work this paper cites.
Sentiment classification of movie reviews using contextual valence shifters
Kennedy, A. and Inkpen, D · 2006
Earlier work this paper cites.
Contextual valence shifters
Polanyi, L. and Zaenen, A · 2006
Earlier work this paper cites.
Learning with compositional semantics as structural inference for subsentential sentiment analysis
Choi, Y. and Cardie, C · 2008
Earlier work this paper cites.
Learning to shift the polarity of words for sentiment classification
Ikeda, D., Takamura, H., Ratinov, L., and Okumura, M · 2008
Earlier work this paper cites.
Review sentiment scoring via a parse-and-paraphrase paradigm
Liu, J. and Seneff, S · 2009
Earlier work this paper cites.
Sentiment classification and polarity shifting
Li, S., Lee, S. Y. M., Chen, Y., Huang, C.-R., and Zhou, G · 2010
Earlier work this paper cites.
A survey on the role of negation in sentiment analysis
Wiegand, M., Balahur, A., Roth, B., Klakow, D., and Montoyo, A · 2010
Earlier work this paper cites.
Lexicon-based methods for sentiment analysis
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., and Stede, M · 2011
Earlier work this paper cites.
Modality and negation: An introduction to the special issue
Morante, R. and Sporleder, C · 2012
Earlier work this paper cites.
Baselines and bigrams: Simple, good sentiment and topic classification
Wang, S. and Manning, C. D · 2012
Cited alongside, same era.
Automatic extraction of contextual valence shifters
Boubel, N., François, T., and Naets, H · 2013
Cited alongside, same era.
Context-dependent computation by recurrent dynamics in prefrontal cortex
Mante, V., Sussillo, D., Shenoy, K. V., and Newsome, W. T · 2013
Cited alongside, same era.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
Cited alongside, same era.
Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
Cited alongside, same era.
Why should I trust you? explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Later among the works it cites.
Visual analysis of hidden state dynamics in recurrent neural networks
Strobelt, H., Gehrmann, S., Huber, B., Pfister, H., Rush, A. M., et al · 2016
Later among the works it cites.
Context-sensitive lexicon features for neural sentiment analysis
Teng, Z., Vo, D.-T., and Zhang, Y · 2016
Later among the works it cites.
Explaining recurrent neural network predictions in sentiment analysis
Arras, L., Montavon, G., Müller, K.-R., and Samek, W · 2017
Later among the works it cites.
Representation of linguistic form and function in recurrent neural networks
Kádár, A., Chrupała, G., and Alishahi, A · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sussillo, D. and Barak, O · 2013
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y · 2014
Cited alongside, same era.
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., Merrienboer, B. v., Gulcehre, C., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
An empirical study on the effect of negation words on sentiment
Zhu, X., Guo, H., Mohammad, S., and Kiritchenko, S · 2014
Cited alongside, same era.
Visualizing and understanding recurrent networks
Karpathy, A., Johnson, J., and Fei-Fei, L · 2015
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Cited alongside, same era.
Kiritchenko, S. and Mohammad, S. M · 2017
Later among the works it cites.
Understanding hidden memories of recurrent neural networks
Ming, Y., Cao, S., Zhang, R., Li, Z., Chen, Y., Song, Y., and Qu, H · 2017
Later among the works it cites.
Challenges in sentiment analysis
Mohammad, S. M · 2017
Later among the works it cites.
Towards bootstrapping a polarity shifter lexicon using linguistic features
Schulder, M., Wiegand, M., Ruppenhofer, J., and Roth, B · 2017
Later among the works it cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Later among the works it cites.
Cueva, C. J. and Wei, X.-X · 2018
Later among the works it cites.
FixedPointFinder: A tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks
Golub, M. and Sussillo, D · 2018
Later among the works it cites.
Universal language model fine-tuning for text classification
Howard, J. and Ruder, S · 2018
Later among the works it cites.
Beyond word importance: Contextual decomposition to extract interactions from LSTMs
Murdoch, W. J., Liu, P. J., and Yu, B · 2018
Later among the works it cites.
An empirical analysis of the role of amplifiers, downtoners, and negations in emotion classification in microblogs
Strohm, F. and Klinger, R · 2018
Later among the works it cites.
Deep learning for sentiment analysis: A survey
Zhang, L., Wang, S., and Liu, B · 2018
Later among the works it cites.
Analysis methods in neural language processing: A survey
Belinkov, Y. and Glass, J · 2019
Later among the works it cites.
A structural probe for finding syntax in word representations
Hewitt, J. and Manning, C. D · 2019
Later among the works it cites.
Gated recurrent units viewed through the lens of continuous time dynamical systems
Jordan, I. D., Sokol, P. A., and Park, I. M · 2019
Later among the works it cites.
How to fine-tune bert for text classification?
Sun, C., Qiu, X., Xu, Y., and Huang, X · 2019
Later among the works it cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., and Le, Q. V · 2019
Later among the works it cites.