Distantly Supervised Lifelong Learning for Large-Scale Social Media Sentiment Analysis
By Rui Xia, Jie Jiang, Huihui He. 2017
63 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Sentiment & Opinion Analysis, Meta & Continual Learning
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Citations in the corpus
- ConTinTin: Continual Learning from Task Instructions (2022, background): “Typical work in NLP includes sentiment classification (Chen et al., 2015; Xia et al., 2017), conversational agents (Lee, 2017), text classification, and question answering (d’Autume et al., 2019), etc. TCL tries to learn distinct tasks sequentially.”
- Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning (2021, methodology): “[64] proposed a LL approach based on voting.”
- Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (2021, methodology): “Xia et al. (2017) presented a LL approach based on voting of individual task classifiers.”
- LAMOL: LAnguage MOdeling for Lifelong Language Learning (2019, background): “To the best of our knowledge, lifelong language learning has been studied in only a few instances; for sentiment analysis (Chen et al., 2015b; Xia et al., 2017), conversational agents (Lee, 2017), word representation learning (Xu et al., 2018), sentence representation learning (Liu et al., 2019),…”
- Continual Learning with Knowledge Transfer for Sentiment Classification (2021, methodology): “[30] presented a LL approach based on voting of individual task classifiers.”
- CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (2021, neutral): “Xia et al. (2017) proposed a voting based approach.”
- Forward and Backward Knowledge Transfer for Sentiment Classification (2019, methodology): “For LLV, we use its first voting method that can improve a past model using future knowledge.”
- Lifelong Learning of Hate Speech Classification on Social Media (2021, methodology): “Xia et al. (2017) combine the”
- LAMAL: LAnguage Modeling Is All You Need for Lifelong Language Learning (2019, methodology): “The tasks have been studied including sentiment analysis (Chen, Ma, and Liu 2015; Xia, Jiang, and He 2017), conversational agent (Lee 2017), word representation learning (Xu et al. 2018), sentence representation learning (Liu, Ungar, and Sedoc 2019), text classification, and question answering…”
- Using the Past Knowledge to Improve Sentiment Classification (2020, methodology): “Xia et al. (2017) proposed a voting method but their method works on the same data from different time periods.”