The Penn Treebank: An Overview

By Ann Taylor, Mitchell P. Marcus, Beatrice Santorini. 2003

438 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/182c4a4074e8577c7ba5cbbc52249e41270c8d64

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Citations in the corpus

Differentiable Architecture Search with Ensemble Gumbel-Softmax (2019, methodology): “A possible reason is that more complex architectures can be searched in DARTS-EGS because of the larger search Variational RHN (Zilly et al., 2016) 67.9 65.4 23 - - manual LSTM (Merity et al., 2017) 60.7 58.8 24 - - manual LSTM + skip connections (Melis et al., 2017) 60.9 58.3 24 - - manual LSTM + 1”
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models (2021, methodology): “Compared to other datasets, such as Penn Treebank (PTB) [Taylor et al., 2003], WikiText features a larger vocabulary and preserves original upper/lower cases, punctuation and numbers.”
Universal Stanford dependencies: A cross-linguistic typology (2014, background): “adjuncts (Taylor et al., 2003).”
Towards Burmese (Myanmar) Morphological Analysis (2019, background): “Compared with the English Penn Treebank, where the inconsistency in POS-tagging is around 3%–5% [27, 40], we consider the quality of released version of the ALT Burmese data is acceptable for NLP research.”
Recurrent Neural Networks With Limited Numerical Precision (2016, background): “The Penn Treebank Corpus [21] (PTC) contains 50 different characters, including English characters, numbers, and punctuations.”
Effective Quantization Methods for Recurrent Neural Networks (2016, methodology): “For language modeling we use Penn Treebank dataset (Taylor et al., 2003), which contains 10K unique words.”
Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation (2020, background): “Structural analysis of sentences, based on a variety of syntactic formalisms (Charniak, 1996; Taylor et al., 2003; De Marneffe et al., 2006; Hock-enmaier and Steedman, 2007; Nivre et al., 2016, 2020, inter alia ), has been the beating heart of NLP pipelines for decades (Klein and Manning, 2003;…”
Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets (2017, methodology): “Techniques for collecting labeled data include recruiting experts for manual annotation [51], extracting relations from readily available sources (e.g., identifying bodies of text in parallel online translations [46, 13]), and automatically generating labels based on user behaviors (e.g., using…”
DATA: Differentiable ArchiTecture Approximation (2019, methodology): “Architecture search on PTB In the language modeling task, DATA is used to search suitable activation function between nodes.”