3,931 research outputs found
Syntactic Fusion: Enhancing Aspect-Level Sentiment Analysis Through Multi-Tree Graph Integration
Recent progress in aspect-level sentiment classification has been propelled
by the incorporation of graph neural networks (GNNs) leveraging syntactic
structures, particularly dependency trees. Nevertheless, the performance of
these models is often hampered by the innate inaccuracies of parsing
algorithms. To mitigate this challenge, we introduce SynthFusion, an innovative
graph ensemble method that amalgamates predictions from multiple parsers. This
strategy blends diverse dependency relations prior to the application of GNNs,
enhancing robustness against parsing errors while avoiding extra computational
burdens. SynthFusion circumvents the pitfalls of overparameterization and
diminishes the risk of overfitting, prevalent in models with stacked GNN
layers, by optimizing graph connectivity. Our empirical evaluations on the
SemEval14 and Twitter14 datasets affirm that SynthFusion not only outshines
models reliant on single dependency trees but also eclipses alternative
ensemble techniques, achieving this without an escalation in model complexity
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis
task that aims to align aspects and corresponding sentiments for
aspect-specific sentiment polarity inference. It is challenging because a
sentence may contain multiple aspects or complicated (e.g., conditional,
coordinating, or adversative) relations. Recently, exploiting dependency syntax
information with graph neural networks has been the most popular trend. Despite
its success, methods that heavily rely on the dependency tree pose challenges
in accurately modeling the alignment of the aspects and their words indicative
of sentiment, since the dependency tree may provide noisy signals of unrelated
associations (e.g., the "conj" relation between "great" and "dreadful" in
Figure 2). In this paper, to alleviate this problem, we propose a Bi-Syntax
aware Graph Attention Network (BiSyn-GAT+). Specifically, BiSyn-GAT+ fully
exploits the syntax information (e.g., phrase segmentation and hierarchical
structure) of the constituent tree of a sentence to model the sentiment-aware
context of every single aspect (called intra-context) and the sentiment
relations across aspects (called inter-context) for learning. Experiments on
four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the
state-of-the-art methods consistently
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training
Aspect-based sentiment analysis (ABSA) aims at automatically inferring the
specific sentiment polarities toward certain aspects of products or services
behind the social media texts or reviews, which has been a fundamental
application to the real-world society. Since the early 2010s, ABSA has achieved
extraordinarily high accuracy with various deep neural models. However,
existing ABSA models with strong in-house performances may fail to generalize
to some challenging cases where the contexts are variable, i.e., low robustness
to real-world environments. In this study, we propose to enhance the ABSA
robustness by systematically rethinking the bottlenecks from all possible
angles, including model, data, and training. First, we strengthen the current
best-robust syntax-aware models by further incorporating the rich external
syntactic dependencies and the labels with aspect simultaneously with a
universal-syntax graph convolutional network. In the corpus perspective, we
propose to automatically induce high-quality synthetic training data with
various types, allowing models to learn sufficient inductive bias for better
robustness. Last, we based on the rich pseudo data perform adversarial training
to enhance the resistance to the context perturbation and meanwhile employ
contrastive learning to reinforce the representations of instances with
contrastive sentiments. Extensive robustness evaluations are conducted. The
results demonstrate that our enhanced syntax-aware model achieves better
robustness performances than all the state-of-the-art baselines. By
additionally incorporating our synthetic corpus, the robust testing results are
pushed with around 10% accuracy, which are then further improved by installing
the advanced training strategies. In-depth analyses are presented for revealing
the factors influencing the ABSA robustness.Comment: Accepted in ACM Transactions on Information System
Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA), a nuanced task in text analysis,
seeks to discern sentiment orientation linked to specific aspect terms in text.
Traditional approaches often overlook or inadequately model the explicit
syntactic structures of sentences, crucial for effective aspect term
identification and sentiment determination. Addressing this gap, we introduce
an innovative model: Syntactic Dependency Enhanced Multi-Task Interaction
Architecture (SDEMTIA) for comprehensive ABSA. Our approach innovatively
exploits syntactic knowledge (dependency relations and types) using a
specialized Syntactic Dependency Embedded Interactive Network (SDEIN). We also
incorporate a novel and efficient message-passing mechanism within a multi-task
learning framework to bolster learning efficacy. Our extensive experiments on
benchmark datasets showcase our model's superiority, significantly surpassing
existing methods. Additionally, incorporating BERT as an auxiliary feature
extractor further enhances our model's performance
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