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Found 9 papers, 6 papers with code
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories
Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task
This paper describes our system, which placed third in the Multilingual Track (subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition.
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks
Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities.
Ranked #12 on
Relation Extraction
on SemEval-2010 Task-8
Improving Arabic Diacritization with Regularized Decoding and Adversarial Training
Arabic diacritization is a fundamental task for Arabic language processing.
Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble
It is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and aspect words.
Ranked #2 on
Aspect-Based Sentiment Analysis (ABSA)
on MAMS
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Enhancing Aspect-level Sentiment Analysis with Word Dependencies
Aspect-level sentiment analysis (ASA) has received much attention in recent years.
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks
End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms.
