activity
20182021
most citedAutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

69 citations · 69 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CL2021

Self-Training with Weak Supervision

Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng +1

State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of d…

cs.CL2020

Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only

Ziyi Liu, Giannis Karamanolakis, Daniel Hsu +1

Health departments have been deploying text classification systems for the early detection of foodborne illness complaints in social media documents such as Yelp restaurant reviews…

cs.CL2020

Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher

Giannis Karamanolakis, Daniel Hsu, Luis Gravano

Cross-lingual text classification alleviates the need for manually labeled documents in a target language by leveraging labeled documents from other languages. Existing approaches…

cs.AI202069 cited

AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

Xin Luna Dong, Xiang He, Andrey Kan +19

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question a…

cs.CL2020

TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories

Giannis Karamanolakis, Jun Ma, Xin Luna Dong

Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce. State-of-the-art approaches for knowledge extraction were each designed for…

cs.LG2019

Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health

Giannis Karamanolakis, Daniel Hsu, Luis Gravano

In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e.g., sentences) of a review may focus on different asp…