Implementation of our papers Joint entity recognition and relation extraction as a multi-head selection problem (Expert Syst. Appl, 2018) and Adversarial training for multi-context joint entity and relation extraction (EMNLP, 2018).

BERT-NER (nert-bert) with google bert https://github.com/google-research.

Multiple-Relations-Extraction-Only-Look-Once. Just look at the sentence once and extract the multiple pairs of entities and their corresponding relations. 端到端联合多关系抽取模型，可用于 http://lic2019.ccf.org.cn/kg 信息抽取。

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

Code for http://lic2019.ccf.org.cn/kg 信息抽取。使用基于 BERT 的实体抽取和关系抽取的端到端的联合模型。

attention based joint model for intent detection and slot filling

Tensorflow and Keras implementation of the state of the art researches in Dialog System NLU

[AAAI 2020] Official repository of FISR.

This repository contains the implementation of an efficient joint beat, downbeat, tempo, and meter tracking system using a compact 1D probabilistic state space and a jump-back reward technique. ICASSP 2022.

[AAAI 2020] Official repository of JSI-GAN.

Joint Models for Longitudinal and Survival Data using MCMC

Paper: A Simple and Effective Neural Model for Joint Word Segmentation and POS Tagging

Joint Models for Longitudinal & Survival Data under Maximum Likelihood

R package for fitting joint models to time-to-event data and multivariate longitudinal data

Materials to reproduce the results of our paper about 3D muscle modelling.

Image Denoising Codes using STROLLR learning, the Matlab implementation of the paper in ICASSP2017

Use eloquent joins in Laravel way, with composite key support.

Joint text classification on multiple levels with multiple labels, using a multi-head attention mechanism to wire two prediction tasks together.

paper notes about joint extraction of entity and relation

One of the main NLU tasks is to understand the intents (sequence classification) and slots (entities within the sequence). This repo help classify both together using Joint Model (multitask model). BERT_SMALL is used which can be changed to any other BERT variant.