Natural Language Grammatical Inference with Recurrent Neural Networks

By Steve Lawrence, C. Lee Giles, Sandiway Fong. 2000

168 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures (2014, unknown): “All the above mentioned architectures have found in recent years a wide application to challenging problems in pattern recognition, such as, for example, in natural language processing [16], chemistry [17], image processing [18], [19], and web mining [20], [21].”
Long short term memory neural network for keyboard gesture decoding (2015, background): “Variants of recurrent neural networks have been applied to various sequence prediction and labelling problems such as speech recognition [7], handwriting recognition [6], grammatical inference [10], among others.”
Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review (2005, methodology): “…(Miller & Giles 1993), as a means for FSM acquisition 6 (e.g. Giles, Horne & Lin 1995) or simply as an analysis tool of the RNN solutions 7 (e.g. Giles & Omlin 1994, Goudreau & Giles 1995, Giles et al. 1997, Lawrence et al. 1998, Lawrence, Giles & Fong 2000, Giles et al. 2001, Bakker 2004).”
Recurrent neural networks for time series classification (2003, background): “Recurrent neural networks (RNNs) are dynamical systems that make efficient use of temporal information in the input sequence, both for classification [6,9,17] as well as for prediction [3,13].”
Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments (2019, background): “Lawrence et al. (2000) Steve Lawrence, C Lee Giles, and Sandiway Fong.”