Results 41 to 50 of about 4,967,798 (324)
Solving Stance Detection on Tweets as Multi-Domain and Multi-Task Text Classification
IEEE Access, 2021 Stance detection on tweets aims at classifying the attitude of tweets towards given targets. Existing work leverage attention-based models to learn target-aware stance representations. While those methods achieve substantial success, most of them usually
Limin Wang, Dexin Wang
doaj +1 more source
Aspect-Controlled Neural Argument Generation
, 2020 We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we train a language model for argument generation
Daxenberger, Johannes +2 more
core +1 more source
Stance Detection in Turkish Tweets [PDF]
CoRR, 2017 Stance detection is a classification problem in natural language processing where for a text and target pair, a class result from the set {Favor, Against, Neither} is expected. It is similar to the sentiment analysis problem but instead of the sentiment of the text author, the stance expressed for a particular target is investigated in stance detection.
openaire +2 more sources
Wearable sensors system for an improved analysis of freezing of gait in Parkinson's disease using electromyography and inertial signals [PDF]
, 2019 We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease.
Gumiero, Alessandro +5 more
core +1 more source
Stance Detection Dataset for Persian Tweets
International Journal of Information and Communication Technology Research, 2022 Stance detection aims to identify an author's stance towards a specific topic which has become a critical component in applications such as fake news detection, claim validation, author profiling, etc.
Mohammad Hadi Bokaei +2 more
doaj
Multi-Perspective Stance Detection [PDF]
CoRRSubjective NLP tasks usually rely on human annotations provided by multiple annotators, whose judgments may vary due to their diverse backgrounds and life experiences. Traditional methods often aggregate multiple annotations into a single ground truth, disregarding the diversity in perspectives that arises from annotator disagreement.
Muscato, Benedetta +4 more
openaire +3 more sources
From Tweets to Stance: An Unsupervised Framework for User Stance Detection on Twitter
, 2023 Current stance inference methods use topic-aligned training data, leaving many unexplored topics due to the lack of training data. Zero-shot approaches utilizing advanced pre-trained Natural Language Inference (NLI) models offer a viable solution when training data is unavailable.
Margherita Gambini +3 more
openaire +3 more sources
Stance Detection and Open Research Avenues
CoRR, 2022 This tutorial aims to cover the state-of-the-art on stance detection and address open research avenues for interested researchers and practitioners. Stance detection is a recent research topic where the stance towards a given target or target set is determined based on the given content and there are significant application opportunities of stance ...
Dilek Küçük, Fazli Can
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Exploiting stance hierarchies for cost-sensitive stance detection of Web documents [PDF]
Journal of Intelligent Information Systems, 2021 This is a pre-print version of the Journal paper published in J Intell Inf Syst (2021) (Springer).
Arjun Roy 0001 +4 more
openaire +2 more sources
ConStance: Modeling Annotation Contexts to Improve Stance Classification
, 2017 Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook.
Friedland, Lisa +4 more
core +1 more source