On learning context-free and context-sensitive languages

By Mikael Bodén, Janet Wiles. 2002

29 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Linguistics & Semantics

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Citations in the corpus

Analysis Methods in Neural Language Processing: A Survey (2018, background): “A long tradition in work on neural networks is to evaluate and analyze their ability to learn different formal languages (Das et al., 1992; Casey, 1996; Gers and Schmidhuber, 2001; Bodén and Wiles, 2002; Chalup and Blair, 2003).”
Neural methods for non-standard data (2004, background): “Recently, an increasing interest in unsupervised recursive processing of structured data can be observed, see e.g. [ 2 , 5] for overviews about this topic.”
Incremental training of first order recurrent neural networks to predict a context-sensitive language (2003, background): “Bodén and Wiles (2000, 2001) did not use the end-of-string marker and updated the weights at the end of each string without resetting at all.”
Architectural Bias in Recurrent Neural Networks: Fractal Analysis (2002, background): “…networks (RNNs) constitute a well established approach for dealing with linguistic data, and the capability of RNNs of processing ﬂnite automata and some context-free and context-sensitive languages is well known (see e.g. (Bod¶en & Wiles, 2002; Forcada & Carrasco, 1995; Ti•no et al., 1998)).”
On Evaluating the Generalization of LSTM Models in Formal Languages (2018, methodology): “Following this approach, Bod´en and Wiles (2002) and Chalup and Blair (2003) studied the stability of the LSTM networks in learning context-free and context-sensitive languages and examined the processing mechanism developed by the hidden states during the training phase.”