1Post-Doctoral Research Center, China Central Depository & Clearing Co., Ltd, Beijing 100033, China 2School of Information, Renmin University of China, Beijing 100871, China 3Blockchain Lab, ChinaBond Finance and Information Technology Co., Ltd, Beijing 100044, China
[Objective] This paper explores methods automatically identifying actionable information from online reviews, aiming to help practitioners improve their follow-up work. [Methods] We defined our task as a sentence-level classification procedure, and proposed a span-based model (SAII). First, we encoded the input sentences based on BERT to generate token-level representation. Then, we enumerated all possible spans from the given sentences and generated informative representations with the help of attention mechanism. Third, we proposed a multi-channel filtering strategy to preserve spans close to the key element prototypes. Finally, we merged the refined span-level and context representations to predict actionable information. [Results] We examined the SAII model with two real-world datasets and found it yielded satisfactory results. Compared with the three best existing models, SAII’s F1 value increased by 7.91%/5.42%, 2.10%/2.73%, and 1.94%/1.46%. [Limitations] More research is needed to evaluate the effectiveness of our new model on multimodal datasets of different domains. [Conclusions] The SAII model could effectively identify actionable information from user-generated contents.
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