CCG Supertagging

Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions

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. In the trending approach of con…

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 appr…

CCG Supertagging as Top-down Tree Generation

Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks

Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studie…

Supertagging with CCG primitives

In CCG and other highly lexicalized grammars, supertagging a sentence{'}s words with their lexical categories is a critical step for efficient parsing. Because of the high degree of lexicalization in these grammars, the …

Hierarchically-Refined Label Attention Network for Sequence Labeling

CRF has been used as a powerful model for statistical sequence labeling. For neural sequence labeling, however, BiLSTM-CRF does not always lead to better results compared with BiLSTM-softmax local classification. This ca…