将复杂句子拆解，提升情感分析准确性
Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis
- 提出句子拆分模型ATOSS，简化复合句结构
- 在ASQP和ACOS任务上超越现有方法
- 可无缝集成到各类情感分析任务中
在方面级情感分析（ABSA）领域，生成式方法已取得显著进展，但提取包含多维度情感表达的四元组仍具挑战。尤其当句子为复合句时，可能包含多个四元组，使提取难度随句式复杂度增加而上升。为此，本文提出面向方面词的句子拆分模型ATOSS，将复杂句子分解为更简洁清晰的形式，从而明确其结构与语义意图。该模型作为即插即用模块，不改变原ABSA模型参数，却显著提升对输入句中关键情感信息的识别能力。大量实验表明，采用ATOSS在ASQP与ACOS两大主要四元组抽取任务上均优于现有方法。
原文摘要 · Abstract (English)
In the domain of Aspect-Based Sentiment Analysis (ABSA), generative methods have shown promising results and achieved substantial advancements. However, despite these advancements, the tasks of extracting sentiment quadruplets, which capture the nuanced sentiment expressions within a sentence, remain significant challenges. In particular, compound sentences can potentially contain multiple quadruplets, making the extraction task increasingly difficult as sentence complexity grows. To address this issue, we are focusing on simplifying sentence structures to facilitate the easier recognition of these elements and crafting a model that integrates seamlessly with various ABSA tasks. In this paper, we propose Aspect Term Oriented Sentence Splitter (ATOSS), which simplifies compound sentence into simpler and clearer forms, thereby clarifying their structure and intent. As a plug-and-play module, this approach retains the parameters of the ABSA model while making it easier to identify essential intent within input sentences. Extensive experimental results show that utilizing ATOSS outperforms existing methods in both ASQP and ACOS tasks, which are the primary tasks for extracting sentiment quadruplets.
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