Direct parsing to sentiment graphs
Not on the map. Why?
Abstract (arXiv)
This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text. We advance the state of the art on 4 out of 5 standard benchmark sets. We release the source code, models and predictions.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 24 Mar 2022.
Builds on
14 of 29 references.
References · 14
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 816
- 2020Revisiting Few-sample BERT Fine-tuningcited by 237
- 2019Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysiscited by 123
- 2020Position-Aware Tagging for Aspect Sentiment Triplet Extractioncited by 90
- 2018Simpler but More Accurate Semantic Dependency Parsingcited by 46
- 2021Structured Sentiment Analysis as Dependency Graph Parsingcited by 28
- 2019A Fine-Grained Sentiment Dataset for Norwegiancited by 23
- 2018MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classificationcited by 17
- 2017A Transition-Based Directed Acyclic Graph Parser for UCCAcited by 13
2 more
Built on by
Citing papers per year: 2022: 1; 2023: 1.
2 papers.