Home Research Papers Datasets State of the Art Pricing

Discover, visualize, and connect AI research papers. Explore the latest trends and insights in artificial intelligence research.

Product

Home
Research Papers
About

Support

Contact
Terms of Service
Privacy Policy

natural-language-processing-9

CCG Supertagging

3260 papers • 126 benchmarks • 313 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

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in ccg-supertagging-9

Trend

Dataset

Best Model

Actions

CCGbank

Libraries

Use these libraries to find ccg-supertagging-9 models and implementations

Datasets

CCGbank

aethel

Subtasks

No subtasks available.

Most implemented papers

Targeted Syntactic Evaluation of Language Models

Tal Linzen, Rebecca Marvin•Sun Aug 26 2018

There is considerable room for improvement over LSTMs in capturing syntax in a language model, and a large gap remained between its performance and the accuracy of human participants recruited online in an experiment using this data set.

450

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Paper Graph

Hierarchically-Refined Label Attention Network for Sequence Labeling

Leyang Cui, Yue Zhang•Thu Aug 22 2019

A hierarchically-refined label attention network is investigated, which explicitly leverages label embeddings and captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention.

98 0

Paper Graph

Semi-Supervised Sequence Modeling with Cross-View Training

Quoc V. Le, Christopher D. Manning, Minh-Thang Luong, Kevin Clark•Fri Sep 21 2018

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, is proposed and evaluated, achieving state-of-the-art results.

341 0

Paper Graph

Something Old, Something New: Grammar-based CCG Parsing with Transformer Models

S. Clark•Mon Sep 20 2021

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 report also documents a 20-year research program, showing how NLP methods have evolved over this time. The staggering accuracy improvements provided by neural models for CCG parsing can be seen as a reflection of the improvements seen in NLP more generally. The report provides a minimal introduction to CCG and CCG parsing, with many pointers to the relevant literature. It then describes the CCG supertagging problem, and some recent work from Tian et al. (2020) which applies Transformer-based models to supertagging with great effect. I use this existing model to develop a CCG multitagger, which can serve as a front-end to an existing CCG parser. Simply using this new multitagger provides substantial gains in parsing accuracy. I then show how a Transformer-based model from the parsing literature can be combined with the grammar-based CCG parser, setting a new state-of-the-art for the CCGbank parsing task of almost 93% F-score for labelled dependencies, with complete sentence accuracies of over 50%.

11 0

Paper Graph

Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions

M. Moortgat, Konstantinos Kogkalidis•Tue Mar 22 2022

This work revisits constructive supertagging from a graph-theoretic perspective, and proposes a framework based on heterogeneous dynamic graph convolutions, aimed at exploiting the distinctive structure of a supertagger’s output space.

10 0

Paper Graph

Keystroke dynamics as signal for shallow syntactic parsing

Barbara Plank•Mon Oct 10 2016

This work postulates that keystroke dynamics contain information about syntactic structure that can inform shallow syntactic parsing, and explores labels derived from keystroke logs as auxiliary task in a multi-task bidirectional Long Short-Term Memory (bi-LSTM).

43 0

Paper Graph

Exploring the Syntactic Abilities of RNNs with Multi-task Learning

Yoav Goldberg, Tal Linzen, Émile Enguehard•Sun Jun 11 2017

It is shown that easily available agreement training data can improve performance on other syntactic tasks, in particular when only a limited amount of training data is available for those tasks, and the multi-task paradigm can be leveraged to inject grammatical knowledge into language models.

28 0

Paper Graph

Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks

Yan Song, Yuanhe Tian•Mon Oct 12 2020

Attentive graph convolutional networks are proposed to enhance neural CCG supertagging through a novel solution of leveraging contextual information.

28 0

Paper Graph

Adding a benchmark result helps the community track progress.

CCG Supertagging | State-of-the-Art