Many efforts have been made in solving the Aspect-based sentiment analysis (ABSA) task.
In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
Ranked #5 on Aspect Sentiment Triplet Extraction on SemEval
Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes.
Ranked #1 on Text Classification on RCV1 (P@1 metric)
Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text.
We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa.
Analyzing people’s opinions and sentiments towards certain aspects is an important task of natural language understanding.
Ranked #4 on Aspect-Based Sentiment Analysis on Sentihood
The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE.