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Found 17 papers, 10 papers with code
AdaTyper: Adaptive Semantic Column Type Detection
A key source for understanding a table is the semantics of its columns.
Machine Learning with DBOS
We recently proposed a new cluster operating system stack, DBOS, centered on a DBMS.
Augmenting Decision Making via Interactive What-If Analysis
The fundamental goal of business data analysis is to improve business decisions using data.
Making Table Understanding Work in Practice
However, we observe that there exists a gap between the performance of these models on these benchmarks and their applicability in practice.
TagRuler: Interactive Tool for Span-Level Data Programming by Demonstration
In this work, we extend the DPBD framework to span-level annotation tasks, arguably one of the most time-consuming NLP labeling tasks.
GitTables: A Large-Scale Corpus of Relational Tables
Existing table corpora primarily contain tables extracted from HTML pages, limiting the capability to represent offline database tables.
Annotating Columns with Pre-trained Language Models
Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information.
Ranked #1 on
Column Type Annotation
on VizNet-Sato-MultiColumn
Leam: An Interactive System for In-situ Visual Text Analysis
Text data analysis is an iterative, non-linear process with diverse workflows spanning multiple stages, from data cleaning to visualization.
Data Programming by Demonstration: A Framework for Interactively Learning Labeling Functions
We operationalize our framework with Ruler, an interactive system that synthesizes labeling rules for document classification by using span-level annotations of users on document examples.
Teddy: A System for Interactive Review Analysis
They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services.
Sato: Contextual Semantic Type Detection in Tables
Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search.
Ranked #2 on
Column Type Annotation
on VizNet-Sato-MultiColumn
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery.
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs.
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration
However, reasoning dynamically about the results of a dimensionality reduction is difficult.
Track Xplorer: A System for Visual Analysis of Sensor-based Motor Activity Predictions
We demonstrate how Track Xplorer helps identify early on possible systemic data errors, effectively track and compare the results of different classifiers, and reason about and pinpoint the causes of misclassifications.
Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks
Rapidly creating effective visualizations using expressive grammars is challenging for users who have limited time and limited skills in statistics and data visualization.
Clustrophile 2: Guided Visual Clustering Analysis
Data scientists need adequate interactive tools to effectively explore and navigate the large clustering space so as to improve the effectiveness of exploratory clustering analysis.
