Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE.
翻译:情感分析研究( ASTE) 已经成为一个新兴的任务, 目的是从某个句子中提取三进制的三进制的调值、 对应的意见术语和相关的情绪极化。 最近, 提出了许多基于神经网络的模型, 带有不同的标签计划, 但几乎所有这些模型都有其局限性: 严重依赖 1 ) 之前假设每个单词仅与单一角色相关联( 例如, 侧面术语, 或意见术语等) 和 2) 单词级互动, 并且将每个观点/ 直径视为一组独立的单词。 因此, 它们在执行复杂的 ASTE 任务上表现不佳, 比如一个与多个角色或一个侧面/ 直观术语相关联的单词。 因此, 我们提出了一个新的方法, Span TAgging 和 Greedy Inference (STAGE), 来提取情绪三进化的情绪, 每个字串联, 也可以同时发挥不同的角色。 为此, 本文将ASTE 任务建立在多级/ 状态上, 一个多级的平级分类问题。 。 STAGAGAGAGE 任期 任期里, 一个更精确的跨跨跨级定义 选项 定义的跨级定义, 定义的跨级定义的跨跨级, 将一个选项, 上, 在前的 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 跨级阶梯级策略上, 上, 上, 上, 上, 标签上, 上, 上, 标上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上, 上。