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Eric's Projects

ggnn icon ggnn

Gated Graph Sequence Neural Networks

giveme5w1h icon giveme5w1h

Extraction of the journalistic five W and one H questions (5W1H) from news articles: who did what, when, where, why, and how?

gnn-comparison icon gnn-comparison

Official Repository of "A Fair Comparison of Graph Neural Networks for Graph Classification", ICLR 2020

gnns-for-nlp icon gnns-for-nlp

Graph Neural Networks for Natural Language Processing tutorial at EMNLP 2019

gpt-3 icon gpt-3

GPT-3: Language Models are Few-Shot Learners

gpt2-chinese icon gpt2-chinese

Chinese version of GPT2 training code, using BERT tokenizer.

hanzi_char_featurizer icon hanzi_char_featurizer

汉字字符特征提取器 (featurizer),提取汉字的特征(发音特征、字形特征)用做深度学习的特征

hawk-projects icon hawk-projects

Project configurations of Hawk and etlpy. xml-format workflow define

haystack icon haystack

:mag: Flexible & Powerful Question Answering Framework. Leveraging latest NLP models for the industry.

hbt icon hbt

A Novel Hierarchical Binary Tagging Framework for Relational Triple Extraction. Accepted by ACL 2020.

heinsen_routing icon heinsen_routing

Official implementation of "An Algorithm for Routing Capsules in All Domains" (Heinsen, 2019) in PyTorch.

hice icon hice

Pytorch Code for ACL'19 "Few-Shot Representation Learning for Out-Of-Vocabulary Words"

hott icon hott

Code for NeurIPS 2019 paper "Hierarchical Optimal Transport for Document Representation"

howmanywordsenses icon howmanywordsenses

Code for experiments with unsupervised estimation of the number of word senses from BERT, ELMo, and fastText.

hpelm icon hpelm

High performance implementation of Extreme Learning Machines (fast randomized neural networks).

hrl-re icon hrl-re

Joint Relation Extraction with Hierarchical Reinforcement Learning

hvpnet icon hvpnet

Code for the NAACL2022 paper "Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction"

hypermine icon hypermine

Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity (CIKM'19)

ie-bert-cnn icon ie-bert-cnn

一个关于百度2019语言与智能技术竞赛信息抽取 (http://lic2019.ccf.org.cn/kg) 模型, 模型采用BERT+CNN。DEMO地址 https://github.com/Wangpeiyi9979/InformationExtractionDemo

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