Type prediction
35 papers with code • 3 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Type prediction
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Libraries
Use these libraries to find Type prediction models and implementations
ManyTypes4TypeScript
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
pytorch/fairseq
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Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
microsoft/CodeBERT
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Findings of the Association for Computational Linguistics 2020
Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks.
Neural Software Analysis
The resulting tools complement and outperform traditional program analyses, and are used in industrial practice.
Entity Identification as Multitasking
Standard approaches in entity identification hard-code boundary detection and type prediction into labels (e. g., John/B-PER Smith/I-PER) and then perform Viterbi.
AffinityNet: semi-supervised few-shot learning for disease type prediction
The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not.
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision
State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings.
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables.
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
SkBlaz/tax2vec
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The use of background knowledge is largely unexploited in text classification tasks.
Learning Semantic Annotations for Tabular Data
The usefulness of tabular data such as web tables critically depends on understanding their semantics.
