activity
20192025
most citedSnippext: Semi-supervised Opinion Mining with Augmented Data

54 citations · 57 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Refining Labeling Functions with Limited Labeled Data

Chenjie Li, Amir Gilad, Boris Glavic +2

Programmatic weak supervision (PWS) significantly reduces human effort for labeling data by combining the outputs of user-provided labeling functions (LFs) on unlabeled datapoints.…

cs.DB2022

Understanding Queries by Conditional Instances

Amir Gilad, Zhengjie Miao, Sudeepa Roy +1

A powerful way to understand a complex query is by observing how it operates on data instances. However, specific database instances are not ideal for such observations: they often…

cs.DB2021★ 2 cited

Putting Things into Context: Rich Explanations for Query Answers using Join Graphs (extended version)

Chenjie Li, Zhengjie Miao, Qitian Zeng +2

In many data analysis applications, there is a need to explain why a surprising or interesting result was produced by a query. Previous approaches to explaining results have direct…

cs.CL2020★ 54 cited

Snippext: Semi-supervised Opinion Mining with Augmented Data

Zhengjie Miao, Yuliang Li, Xiaolan Wang +1

Online services are interested in solutions to opinion mining, which is the problem of extracting aspects, opinions, and sentiments from text. One method to mine opinions is to lev…

cs.DB2019★ 1 cited

Explaining Wrong Queries Using Small Examples

Zhengjie Miao, Sudeepa Roy, Jun Yang

For testing the correctness of SQL queries, e.g., evaluating student submissions in a database course, a standard practice is to execute the query in question on some test database…