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| Identifying Actionable Information from Online Reviews |
Shang Lili1,2,Tang Huayun3,Wang Yanzhao3,Zuo Meiyun2( ) |
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 |
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Abstract [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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Received: 11 February 2022
Published: 03 February 2023
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| Fund:Fundation of Beijing Key Laboratory of Green Development Decision Making Based on Big Data(Grant No. dm202103) |
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Corresponding Authors:
Zuo Meiyun，ORCID：0000-0002-5281-5071
E-mail: zuomy@ruc.edu.cn
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