Open AccessPosted Content
Relational Graph Attention Network for Aspect-based Sentiment Analysis
Reads0
Chats0
TLDR
This paper defines a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree and proposes a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction.Abstract:
Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words. However, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections. In this paper, we address this problem by means of effective encoding of syntax information. Firstly, we define a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree. Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction. Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the experimental results confirm that the connections between aspects and opinion words can be better established with our approach, and the performance of the graph attention network (GAT) is significantly improved as a consequence.read more
Citations
More filters
Posted Content
An Attentive Survey of Attention Models
TL;DR: A taxonomy that groups existing techniques into coherent categories in attention models is proposed, and how attention has been used to improve the interpretability of neural networks is described.
Posted Content
Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa
TL;DR: This paper compares the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree and reveals that the FT-RoberTa Induced Tree is more sentiment-word-oriented and could benefit theABSA task.
Proceedings ArticleDOI
Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble.
Yuanhe Tian,Guimin Chen,Yan Song +2 more
TL;DR: This paper proposes an approach to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks (T-GCN), where attention is used in T- GCN to distinguish different edges in the graph and attentive layer ensemble is proposed to comprehensively learn from different layers of T-GCn.
Journal ArticleDOI
Knowledge-enabled BERT for aspect-based sentiment analysis
Anping Zhao,Yu Yu +1 more
TL;DR: This work proposes a knowledge-enabled language representation model BERT that leverages the additional information from a sentiment knowledge graph by injecting sentiment domain knowledge into the language representation models, which obtains the embedding vectors of entities in the sentiment knowledge graphs and words in the text in a consistent vector space.
Proceedings ArticleDOI
Inducing Target-Specific Latent Structures for Aspect Sentiment Classification
TL;DR: This work proposes gating mechanisms to dynamically combine information from word dependency graphs and latent graphs which are learned by self-attention networks to complement supervised syntactic features with latent semantic dependencies inpect-level sentiment analysis.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Proceedings ArticleDOI
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TL;DR: BERT as mentioned in this paper pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Proceedings ArticleDOI
The Stanford CoreNLP Natural Language Processing Toolkit
Christopher D. Manning,Mihai Surdeanu,John Bauer,Jenny Rose Finkel,Steven Bethard,David McClosky +5 more
TL;DR: The design and use of the Stanford CoreNLP toolkit is described, an extensible pipeline that provides core natural language analysis, and it is suggested that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.
Related Papers (5)
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
Binxuan Huang,Kathleen M. Carley +1 more