natural-language-processing-5
Table annotation
3260 papers • 126 benchmarks • 313 datasets
Table annotation is the task of annotating a table with terms/concepts from knowledge graph or database schema. Table annotation is typically broken down into the following five subtasks: Cell Entity Annotation (CEA) Column Type Annotation (CTA) Column Property Annotation (CPA) Table Type Detection Row Annotation The SemTab challenge is closely related to the Table Annotation problem. It is a yearly challenge which focuses on the first three tasks of table annotation and its purpose is to benchmark different table annotation systems.
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Sherlock: A Deep Learning Approach to Semantic Data Type Detection
César A. Hidalgo, Madelon Hulsebos, Tim Kraska, cCaugatay Demiralp, K. Hu, Michiel A. Bakker, Emanuel Zgraggen, Arvind Satyanarayan•Fri May 24 2019
Sherlock is introduced, a multi-input deep neural network for detecting semantic types that achieves a support-weighted F$_1 score of $0.89, exceeding that of machine learning baselines, dictionary and regular expression benchmarks, and the consensus of crowdsourced annotations.
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