CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank

By Julia Hockenmaier, Mark Steedman. 2007

471 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Language and Linguistics, Evaluation & Benchmarks

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Citations in the corpus

Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (2018, methodology): “These include image-caption matching with MSCOCO (Lin et al., 2014), following Kiela et al. (2018); the small-to-medium-data textunderstanding tasks collected in NLI format by Poliak et al. (2018); ordinal common sense inference (Zhang et al., 2017); POS tagging on the Penn Treebank (Marcus et al., ”
Targeted Syntactic Evaluation of Language Models (2018, methodology): “We used CCG-Bank (Hockenmaier and Steedman, 2007) as our CCG corpus.”
Linguistic Knowledge and Transferability of Contextual Representations (2019, methodology): “We use CCGbank (Hockenmaier and Steedman, 2007), a conversion of the PTB into CCG derivations.”
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis (2018, methodology): “For CCG supertagging, we use CCG Bank (Hockenmaier & Steedman, 2007), which is based on PTB WSJ. CCG supertagging provides ﬁne-grained information about the role of each word in its larger syntactic context and is considered almost parsing, since sequences of tags map sentences to small subsets of…”
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (2019, methodology): “Categorial Grammar Bank corpus (Hockenmaier and Steedman, 2007), motivated by Wilcox et al.”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, methodology): “…& Rajkumar, 2009, 2012) presents a broad coverage English surface realizer, based on Combinatory Categorial Grammar (ccg; Steedman, 2000), relying on a corpus of ccg representations derived from the Penn Treebank (Hockenmaier & Steedman, 2007) and using statistical language models for reranking.”
Exploring and Predicting Transferability across NLP Tasks (2020, methodology): “…labeling (eleven tasks): We experiment with eleven sequence labeling tasks used by Liu et al. (2019a), including CCG supertagging with CCGbank (CCG; Hockenmaier and Steedman, 2007); part-of-speech tagging with the Penn Treebank (POS-PTB; Marcus et al., 1993) and the Universal Dependencies English…”
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models (2020, methodology): “…(Tenney et al., 2019b), and the SentEval probing suite (Conneau and Kiela, 2018), as well as other individual tasks including CCG supertagging (Hockenmaier and Steedman, 2007), SocialIQA (Sap et al., 2019), and CommonsenseQA (Talmor et al., 2019). jiant is also the ofﬁcial baseline codebase…”
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension (2019, methodology): “CCG Supertagging We train a model to predict the Combinatory Categorial Grammar (CCG) su-pertag for each word, with sentences from CCG-Bank (Hockenmaier and Steedman, 2007).”
Identifying beneficial task relations for multi-task learning in deep neural networks (2017, methodology): “We use the standard splits for CCG super-tagging from the CCGBank (Hockenmaier and Steedman, 2007).”
Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020, methodology): “CCG CCGbank (Hockenmaier and Steedman, 2007) is a task that is a translation of the Penn Treebank into a corpus of Combinatory Categorial Grammar (CCG) derivations.”
Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis (2018, background): “, 1993) and Combinatorial Categorical Grammar (CCG) supertagging on CCG Bank (Hockenmaier and Steedman, 2007).”
Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling (2018, background): “These include image-caption matching with MSCOCO (Lin et al., 2014), following Kiela et al. (2018); the small-to-medium-data textunderstanding tasks collected in NLI format by Poliak et al. (2018); ordinal common sense inference (Zhang et al., 2017); POS tagging on the Penn Treebank (Marcus et al., ”
Non-entailed subsequences as a challenge for natural language inference (2018, methodology): “Our pre-training tasks were: NLI using the MNLI corpus, combinatory categorial grammar (CCG) supertagging using tags from CCGbank (derived from the Penn Treebank) (Hockenmaier and Steedman, 2007), image generation from captions using the MS COCO data set (Lin et al.”
A* CCG Parsing with a Supertag and Dependency Factored Model (2017, methodology): “On English CCGbank test data, our model with this technique achieves 88.8% and 94.0% in terms of labeled and unlabeled F1, which mark the best scores so far.”
A Survey on Deep Semi-Supervised Learning (2021, methodology): “Sequence learning IMDB [98], Rotten Tomatoes [102], 20 Newsgroups [116], DBpedia [96], CoNLL 2003 NER task [117], CoNLL 2000 Chunking task [118], Twitter POS Dataset [119], [120], Universal Dependencies(UD) [121], Combinatory Categorial Grammar (CCG) supertagging [122] [123], [124], [125], [126]”