Graph-based semi-supervised learning is appealing when labels are scarce but large amounts of
unlabeled data are available. These methods typically use a heuristic strategy to construct the
graph based on some fixed data representation, independently of the available labels. In this pa-
per, we propose to jointly learn a data representation and a graph from both labeled and unlabeled
data such that (i) the learned representation indirectly encodes the label information injected into
the graph, and (ii) the graph provides a smooth topology with respect to the transformed data.
Plugging the resulting graph and representation into existing graph-based semi-supervised learn-
ing algorithms like label spreading and graph convolutional networks, we show that our approach
outperforms standard graph construction methods on both synthetic data and real datasets.

Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning

Graph-based Methods for Natural Language Processing (TextGraphs)

technical paper

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Mariana Vargas Vieyra

1

SHORT BIO

Presentations

Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES