Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

He Zhao 0003

dblp:98/3487-3 · DBLP profile ↗
← Back
4ranked-venue papers
2as first author
1since 2021 · last 2021
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-author · 1 since 2021Artificial intelligence and machine learning · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 50% Data mining · 50%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › document analysis › scholarly text analysis
citation analysis		 0.412019
A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature · EMNLP/IJCNLP (1) 2019
Information retrieval
citation analysis		 0.412019
A Context-based Framework for Resource Citation Classification in Scientific Literatures · SIGIR 2019
Data mining › text mining
text classification		 0.412019
A Context-based Framework for Resource Citation Classification in Scientific Literatures · SIGIR 2019

Methods — techniques the papers use, named apart from their topics

neural network · 0.4context-based modeling · 0.4
YearPublicationVenuePosition
2021 Improving On-line Scientific Resource Profiling by Exploiting Resource Citation Information in the Literature
Anqing Zheng, He Zhao 0003, Zhunchen Luo, Chong Feng 0001, Yuming Ye
 Inf. Process. Manag.2
2019 A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature
abstract
He Zhao, Zhunchen Luo, Chong Feng, Anqing Zheng, Xiaopeng Liu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
He Zhao 0003, Zhunchen Luo, Chong Feng 0001, Anqing Zheng
 EMNLP/IJCNLP (1)1
2019 A Shallow Semantic Parsing Framework for Event Argument Extraction
Zhunchen Luo, Guobin Sui, He Zhao 0003
 KSEM (2)3
2019 A Context-based Framework for Resource Citation Classification in Scientific Literatures
abstract
In this paper, we introduce the task of resource citation classification for scientific literature using a context-based framework. This task is to analyze the purpose of citing an on-line resource in scientific text by modeling the role and function of each resource citation. It can be incorporated into resource indexing and recommendation systems to help better understand and classify on-line resources in scientific literature. We propose a new annotation scheme for this task and develop a dataset of 3,088 manually annotated resource citations. We adopt a neural-based model to build the classifiers and apply them on the large ARC dataset to examine the revolution of scientific resources from trends in their function over time.
He Zhao 0003, Zhunchen Luo, Chong Feng 0001, Yuming Ye
 SIGIR1