Junlang Qian, Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Zepeng Zhai, Kezhi Mao. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Most previous studies on Structured Sentiment Analysis (SSA) have cast it as a problem of bi-lexical dependency parsing, which cannot address issues of overlap and discontinuity simultaneously.In this paper, we propose a nichetargeting and effective solution.Our approach involves creating a novel bi-lexical dependency parsing graph, which is then converted to a unified 2D table-filling scheme, namely USSA.The proposed scheme resolves the kernel bottleneck of previous SSA methods by utilizing 13 different types of relations.In addition, to closely collaborate with the USSA scheme, we have developed a model that includes a proposed bi-axial attention module to effectively capture the correlations among relations in the rows and columns of the table.Extensive experimental results on benchmark datasets demonstrate the effectiveness and robustness of our proposed framework, outperforming state-ofthe-art methods consistently 1 .

In the NLP domain, Aspect-Based Sentiment Analysis (ABSA) has gained significant attention in recent years due to its ability to perform fine-grained sentiment analysis. A challenging task in ABSA is Aspect Sentiment Quadruplet Extraction (ASQE), which involves the extraction of aspect terms and their associated opinion terms, sentiment polarities, and categories in the form of quadruplets. However, existing studies have ignored the strong dependence among the multiple subtasks involved in ASQE. In this paper, we propose a novel Enhanced Machine Reading Comprehension (EMRC) method and formalize ASQE task as a multi-turn MRC task. Our EMRC effectively learns and utilizes the relationships among different subtasks by incorporating previously generated query answers into the current queries. We design a hierarchical category classification strategy to perform the category prediction in a structured manner, enabling the model to tackle intricate categories with ease. Furthermore, we employ the bi-directional attention mechanism, i.e., context-to-query and query-to-context attentions, to map the context into a task-aware representation. We conduct extensive experiments on two benchmark datasets. The results demonstrate that EMRC outperforms the state-of-art baselines. The source code is publicly available at https://github.com/Little-Yeah/EMCR.

Aspect Sentiment Triplet Extraction (ASTE) is an emerging sentiment analysis task.Most of the existing studies focus on devising a new tagging scheme that enables the model to extract the sentiment triplets in an end-to-end fashion.However, these methods ignore the relations between words for ASTE task.In this paper, we propose an Enhanced Multi-Channel Graph Convolutional Network model (EMC-GCN) to fully utilize the relations between words.Specifically, we first define ten types of relations for ASTE task, and then adopt a biaffine attention module to embed these relations as an adjacent tensor between words in a sentence.After that, our EMC-GCN transforms the sentence into a multi-channel graph by treating words and the relation adjacent tensor as nodes and edges, respectively.Thus, relationaware node representations can be learnt.Furthermore, we consider diverse linguistic features to enhance our EMC-GCN model.Finally, we design an effective refining strategy on EMC-GCN for word-pair representation refinement, which considers the implicit results of aspect and opinion extraction when determining whether word pairs match or not.Extensive experimental results on the benchmark datasets demonstrate that the effectiveness and robustness of our proposed model, which outperforms state-of-the-art methods significantly.

Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, which was recently formalized as an effective machine reading comprehension (MRC) based framework.However, when facing multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms.In this paper, we propose a novel COntext-Masked MRC (COM-MRC) framework for ASTE.Our COM-MRC framework comprises three closely-related components: a context augmentation strategy, a discriminative model, and an inference method.Specifically, a context augmentation strategy is designed by enumerating all masked contexts for each aspect term.The discriminative model comprises four modules, i.e., aspect and opinion extraction modules, sentiment classification and aspect detection modules.In addition, a two-stage inference method first extracts all aspects and then identifies their opinions and sentiment through iteratively masking the aspects.Extensive experimental results on benchmark datasets show the effectiveness of our proposed COM-MRC framework, which outperforms state-of-the-art methods consistently 1 .