47 citations · 150 across the 9 of their papers we have counts for
7 papers · 1 filter
Unconfounded Propensity Estimation for Unbiased Ranking
Dan Luo, Lixin Zou, Qingyao Ai +4
The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems. Among existing solutions, automatic ULTR algorithms that…
Model-based Unbiased Learning to Rank
Dan Luo, Lixin Zou, Qingyao Ai +3
Unbiased Learning to Rank (ULTR) that learns to rank documents with biased user feedback data is a well-known challenge in information retrieval. Existing methods in unbiased learn…
StruBERT: Structure-aware BERT for Table Search and Matching
Mohamed Trabelsi, Zhiyu Chen, Shuo Zhang +2
A large amount of information is stored in data tables. Users can search for data tables using a keyword-based query. A table is composed primarily of data values that are organize…
WTR: A Test Collection for Web Table Retrieval
Zhiyu Chen, Shuo Zhang, Brian D. Davison
We describe the development, characteristics and availability of a test collection for the task of Web table retrieval, which uses a large-scale Web Table Corpora extracted from th…
Neural ranking models for document retrieval
Mohamed Trabelsi, Zhiyu Chen, Brian D. Davison +1
Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-craf…
Table Search Using a Deep Contextualized Language Model
Zhiyu Chen, Mohamed Trabelsi, Jeff Heflin +2
Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining task…
Leveraging Schema Labels to Enhance Dataset Search
Zhiyu Chen, Haiyan Jia, Jeff Heflin +1
A search engine's ability to retrieve desirable datasets is important for data sharing and reuse. Existing dataset search engines typically rely on matching queries to dataset desc…