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acm-challenge-workbook

《挑战程序设计竞赛》习题册攻略

albert_ner_keras

利用ALBERT和BI-LSTM，在Keras中实现序列标注算法。

anago

Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.

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.

biomedical-relation-classification

bionlp-corpus

Repository used to collect biomedical corpus on the Internet!

chinese-word-vectors

100+ Chinese Word Vectors 上百种预训练中文词向量

clrs

Some exercises and problems in Introduction to Algorithms 3rd edition.

ddicpi-

多分类 关系抽取

eda_nlp

Data augmentation for NLP, presented at EMNLP 2019

git

grokking_algorithms

Code for the book Grokking Algorithms (https://amzn.to/29rVyHf)

hahaspanish

西班牙语情感幽默分类-集成模型

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.

keras-gcn

Keras implementation of Graph Convolutional Networks

leetcode

overview-of-the-biocreative-v-chemical-disease-relation-cdr-task

BioCreative-V CDR关系抽取任务综述

scibert

A BERT model for scientific text.

usc-ds-relationextraction

Distantly Supervised Relation Extraction

xlnet

XLNet: Generalized Autoregressive Pretraining for Language Understanding