https://github.com/exaco/exaasc
공식 코드ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Stance detection is the extraction of a subject's reaction to a claim made by a primary actor. It is a core part of a set of approaches to fake news assessment.
Example:
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language
Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
Words That Unite The World: A Unified Framework for Deciphering Central Bank Communications Globally
A Retrospective Analysis of the Fake News Challenge Stance Detection Task
TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings
Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification
STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network
Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets
Stance Prediction for Contemporary Issues: Data and Experiments
#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
Stance Detection: A Practical Guide to Classifying Political Beliefs in Text
STEM: Unsupervised STructural EMbedding for Stance Detection
A Challenge Dataset and Effective Models for Conversational Stance Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.