The Penn Chinese TreeBank: Phrase structure annotation of a large corpus

By Nianwen Xue, Fei Xia, Fu-Dong Chiou, Martha Palmer. 2005

755 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change (2016, background): “0 × 10 10 1950-1999 (Xue et al., 2005) Table 1: Six large historical datasets from various languages and sources are used.”
Self-Training PCFG Grammars with Latent Annotations Across Languages (2009, methodology): “CTB6 includes both news articles and transcripts of broadcast news.”
Towards Robust Linguistic Analysis using OntoNotes (2013, methodology): “However, unlike the previous extensions of the Treebank, in addition to using roughly a third of the same WSJ subcorpus, OntoNotes also added several other genres, and covers two other languages — Chinese and Arabic: portions of the Chinese Treebank (Xue et al., 2005) and the Arabic Treebank (Maamou”
In-Order Transition-based Constituent Parsing (2017, positive): “The in-order parser achieves the best results under hte fully-Model 86.9 Top-down parser 86.9 Bottom-up parser 87.5 In-order parser 88.0 semi-supervision Zhu et al. (2013) 85.6 Wang and Xue (2014) 86.3 Wang et al. (2015) 86.6 Table 6: Final results on test set of CTB. supervised setting.”
Feature Optimization for Constituent Parsing via Neural Networks (2015, methodology): “We conducted experiments on the Penn Chinese Treebank (CTB) version 5.1 (Xue et al., 2005) and the Wall Street Journal (WSJ) portion of Penn English Treebank (Marcus et al., 1993).”
Universal Stanford dependencies: A cross-linguistic typology (2014, background): “Conversely, the Penn Chinese Treebank does try to make an adjunct/argument distinction (Xue et al., 2005) and effectively PropBank (Palmer et al., 2005) adds an argument/adjunct distinction overlay to the English Penn Treebank. of multiple objects, the theme/patient argument is the direct object.”
Fast and Accurate Shift-Reduce Constituent Parsing (2013, methodology): “1 of the Penn Chinese Treebank (CTB) (Xue et al., 2005).”
Optimizing Chinese Word Segmentation for Machine Translation Performance (2008, methodology): “Since the word segmentation standard under consideration (Chinese Treebank (Xue et al., 2005)) was neither speciﬁcally designed nor optimized for MT, it seems reasonable to investigate whether any segmentation granularity in continuum between character-level and CTB-style segmentation is more…”
OntoNotes : A Large Training Corpus for Enhanced Processing (2017, background): “The Tsinghua Chinese Treebank TCT (Zhou, 2003) contains some 2 million Chinese characters, of which half has been tree- banked, and manually annotated for syntactic and certain semantic relations, such as causality and conditionals.”
Neural Word Segmentation with Rich Pretraining (2017, methodology): “In addition to CTB6, which has been the most commonly adopted by recent segmentation research, we additionally evaluate our results on the SIGHAN 2005 bakeoff and Weibo datasets, to examine cross domain robustness.”
A Survey of the Usages of Deep Learning for Natural Language Processing (2020, background): “This allowed for better predictions, generating stateof-the-art scores on the Stanford Dependency Treebank [109], as well as state-of-the-art results on the CTB5 Chinese data set [110].”
Unsupervised Recurrent Neural Network Grammars (2019, methodology): “While still a subset of the full corpus (which has 30M sentences), this dataset is two orders of magnitude larger than PTB. Experiments on Chinese utilize version 5.1 of the Chinese Penn Treebank (CTB) (Xue et al., 2005), with the same splits as in Chen and Manning (2014).”
Lattice-Based Transformer Encoder for Neural Machine Translation (2019, methodology): “For Zh-En task, we followed Su et al. (2017) to use the toolkit 2 to train segmenters on PKU, MSR (Emerson, 2005), and CTB corpora (Xue et al., 2005), then we generated word lattices with different segmented training data.”
Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder (2017, methodology): “Whereas Eriguchi et al. (2016) use HPSG trees, we use phrase-structure trees as in the Penn Chinese Treebank (Xue et al., 2005).”
Chinese NER Using Lattice LSTM (2018, methodology): “Gold seg Yang et al. (2016) 65.”
Deep Multitask Learning for Semantic Dependency Parsing (2017, methodology): “PAS annotations are also available for the Penn Chinese Treebank (Xue et al., 2005).”