CCG Supertagging
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.
Example:
| Vinken | , | 61 | years | old |
|---|---|---|---|---|
| N | , | N/N | N | (S[adj]\ NP)\ NP |
Benchmarks
These leaderboards are used to track progress in CCG Supertagging
| Trend | Dataset | Best Model | Paper | Code | Compare |
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CCGbank
Most implemented papers
Targeted Syntactic Evaluation of Language Models
BeckyMarvin/LM_syneval
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EMNLP 2018
We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence.
Semi-Supervised Sequence Modeling with Cross-View Training
tensorflow/models
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EMNLP 2018
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.
Hierarchically-Refined Label Attention Network for Sequence Labeling
CRF has been used as a powerful model for statistical sequence labeling.
Something Old, Something New: Grammar-based CCG Parsing with Transformer Models
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.
Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions
konstantinoskokos/dynamic-graph-supertagging
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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 as signal for shallow syntactic parsing
Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing.
Exploring the Syntactic Abilities of RNNs with Multi-task Learning
Recent work has explored the syntactic abilities of RNNs using the subject-verb agreement task, which diagnoses sensitivity to sentence structure.
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks
cuhksz-nlp/NeST-CCG
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EMNLP 2020
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.
