Type prediction
41 papers with code • 3 benchmarks • 1 datasets
Libraries
Use these libraries to find Type prediction models and implementationsMost implemented papers
SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain.
Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python
It learns to discriminate between similar and dissimilar types in a high-dimensional space, which results in clusters of types.
TCN: Table Convolutional Network for Web Table Interpretation
Existing work linearize table cells and heavily rely on modifying deep language models such as BERT which only captures related cells information in the same table.
Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases
Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species.
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.
Text2Chart: A Multi-Staged Chart Generator from Natural Language Text
Firstly, it identifies the axis elements of a chart from the given text known as x and y entities.
Point-of-Interest Type Prediction using Text and Images
Point-of-interest (POI) type prediction is the task of inferring the type of a place from where a social media post was shared.
AiTLAS: Artificial Intelligence Toolbox for Earth Observation
The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets.
Beyond Duplicates: Towards Understanding and Predicting Link Types in Issue Tracking Systems
For instance, Duplication links tend to represent simpler issue graphs often with two components and Composition links present the highest amount of hierarchical tree structures (97. 7%).
Predicting Issue Types with seBERT
Pre-trained transformer models are the current state-of-the-art for natural language models processing.