HunterHeidy's Stars
HWHU/EntitiesRelationExtraction
Extract relationship of two named entities, namely miRNA and gene from bio-medical journal articles.
HunterHeidy/keras-gcn
Keras implementation of Graph Convolutional Networks
CrazilyCode/RelationExtraction
CrazilyCode/SemEval2010-Task8
HunterHeidy/HASOC2020
The large fraction of hate speech and other offensive and objectionable content online poses a huge challenge to societies. Offensive language such as insulting, hurtful, derogatory or obscene content directed from one person to another person and open for others undermines objective discussions. Such type of language can be more increasingly found on the web and can lead to the radicalization of debates. Public opinion forming requires rational critical discourse (Habermas 1984). Objectionable content can pose a threat to democracy. At the same time, open societies need to find an adequate way to react to such content without imposing rigid censorship regimes. As a consequence, many platforms of social media websites monitor user posts. This leads to a pressing demand for methods to automatically identify suspicious posts. Online communities, social media enterprises and technology companies have been investing heavily in technology and processes to identify offensive language in order to prevent abusive behavior in social media. HASOC provides a forum and a data challenge for multilingual research on the identification of problematic content. This year, we offer again 2 sub-tasks for each language such as English, German and Hindi, alltogether over 10.000 annotated tweets from Twitter. Participants in this year’s shared task can choose to participate in one or two of the subtasks. Participants can look at the openly available data of HASOC 2019: https://hasocfire.github.io/hasoc/2019/dataset.html Tasks There are two sub-tasks in each of the languages. Below is a brief description of each task. Sub-task A: Identifying Hate, offensive and profane content This task focus on Hate speech and Offensive language identification offered for English, German, and Hindi. Sub-task A is coarse-grained binary classification in which participating system are required to classify tweets into two classes, namely: Hate and Offensive (HOF) and Non- Hate and offensive (NOT). (NOT) Non Hate-Offensive - This post does not contain any Hate speech, profane, offensive content. (HOF) Hate and Offensive - This post contains Hate, offensive, and profane content. Sub-task B: Discrimination between Hate, profane and offensive posts This sub-task is a fine-grained classification offered for English, German, and Hindi. Hate-speech and offensive posts from the sub-task A are further classified into three categories: (HATE) Hate speech:- Posts under this class contain Hate speech content. (OFFN) Offenive:- Posts under this class contain offensive content. (PRFN) Profane:- These posts contain profane words. Categories Explanation: HATE SPEECH: Describing negative attributes or deficiencies to groups of individuals because they are members of a group (e.g. all poor people are stupid). Hateful comment toward groups because of race, political opinion, sexual orientation, gender, social status, health condition or similar. OFFENSIVE: Posts which are degrading, dehumanizing,insulting an individual,threatening with violent acts are categorized into OFFENSIVE category. PROFANITY: Unacceptable language in the absence of insults and abuse. This typically concerns the usage of swearwords (Scheiße, Fuck etc.) and cursing (Zur Hölle! Verdammt! etc.) are categorized into this category.
HunterHeidy/leetcode
HunterHeidy/DDICPI-
多分类 关系抽取
HunterHeidy/HahaSpanish
西班牙语情感幽默分类-集成模型
leetcode-notebook/leetcode-notebook.github.io
关于刷题小组及加入方式。
yogykwan/acm-challenge-workbook
《挑战程序设计竞赛》习题册攻略
tkipf/keras-gcn
Keras implementation of Graph Convolutional Networks
ufal/treex
Treex NLP framework
CyberZHG/CLRS
Some exercises and problems in Introduction to Algorithms 3rd edition.
google-research/google-research
Google Research
dterg/biomedical_corpora
Table compiling the list of biomedically-related corpora available for named entity recognition (and some also suitable for association detection). First version has was published as part of the paper: Dieter Galea, Ivan Laponogov, Kirill Veselkov; Exploiting and assessing multi-source data for supervised biomedical named entity recognition, Bioinformatics, bty152, https://doi.org/10.1093/bioinformatics/bty152 . If you would like to add other (or your) corpora, please submit a pull request and I'll happily approve it.
BaderLab/Biomedical-Corpora
A collection of annotated biomedical corpora, which can be used for training supervised machine learning methods for various tasks in biomedical text-mining and information extraction.
allenai/scibert
A BERT model for scientific text.
Aitslab/BioNLP
Repository for student projects within biomedical text mining from Lund University
CyberZHG/keras-trans-mask
Remove and restore masks for layers that do not support masking
bojone/crf
keras implementation of conditional random field
shaoxiongji/knowledge-graphs
A collection of research on knowledge graphs
rubenkruiper/FOBIE
FOBIE dataset and code for Semi-Open Relation Extraction, applied to Biology for Computer-Aided Biomimetics.
jeniyat/WNUT_2020_NER
This repository will contain the data and codes for WNUT 2020 NER task
lancopku/Graph-to-seq-comment-generation
Code for the paper ``Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model''
cerebroai/reformers
Efficient Transformers for research, PyTorch and Tensorflow using Locality Sensitive Hashing
google/trax
Trax — Deep Learning with Clear Code and Speed
ari-holtzman/degen
Official Repository for "The Curious Case of Neural Text Degeneration"
gildofabregat/RDD-Relation-Extraction-2018
Use of deep learning for the relation extraction in the RDD corpus
jasonwei20/eda_nlp
Data augmentation for NLP, presented at EMNLP 2019
caldreaming/CAIL
法研杯CAIL2019阅读理解赛题参赛模型