Type prediction

28 papers with code • 3 benchmarks • 1 datasets

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Use these libraries to find Type prediction models and implementations

Most implemented papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

google-research/bert 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 26 Jul 2019

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 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

superli3/codenavi 16 Nov 2020

The resulting tools complement and outperform traditional program analyses, and are used in industrial practice.

Entity Identification as Multitasking

karlstratos/mention2vec WS 2017

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

BeautyOfWeb/AffinityNet 22 May 2018

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

dair-iitd/kbi ACL 2018

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

alan-turing-institute/SemAIDA 4 Nov 2018

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 1 Feb 2019

The use of background knowledge is largely unexploited in text classification tasks.

Learning Semantic Annotations for Tabular Data

alan-turing-institute/SemAIDA 30 May 2019

The usefulness of tabular data such as web tables critically depends on understanding their semantics.