8 papers with code • 1 benchmarks • 2 datasets
Combinatory Categorical Grammar (CCG; Steedman, 2000) is a highly lexicalized formalism. The standard parsing model of Clark and Curran (2007) uses over 400 lexical categories (or supertags), compared to about 50 part-of-speech tags for typical parsers.
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We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
This report describes the parsing problem for Combinatory Categorial Grammar (CCG), showing how a combination of Transformer-based neural models and a symbolic CCG grammar can lead to substantial gains over existing approaches.
The syntactic categories of categorial grammar formalisms are structured units made of smaller, indivisible primitives, bound together by the underlying grammar's category formation rules.
Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing.
Recent work has explored the syntactic abilities of RNNs using the subject-verb agreement task, which diagnoses sensitivity to sentence structure.
Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly.