Name
Abstract | ||
|---|---|---|
BSTRACTTargeted Opinion Word Extraction (TOWE) is a subtask of aspect-based sentiment analysis, which aims to identify the correspondingopinion terms for given opinion targets in a review. To solve theTOWE task, recent works mainly focus on learning the target-aware context representation that infuses target information intocontext representation by using various neural networks. However,it has been unclear how to encode the target information to BERT,a powerful pre-trained language model. In this paper, we proposea novel TOWE model, RABERT (Relation-Aware BERT), that canfully utilize BERT to obtain target-aware context representations.To introduce the target information into BERT layers clearly, wedesign a simple but effective encoding method that adds targetmarkers indicating the opinion targets to the sentence. In addi-tion, we find that the neighbor word information is also importantfor extracting the opinion terms. Therefore, RABERT employs thetarget-sentence relation network and the neighbor-aware relationnetwork to consider both the opinion target and the neighbor wordsinformation. Our experimental results on four benchmark datasetsshow that RABERT significantly outperforms the other baselinesand achieves state-of-the-art performance. We also demonstrate theeffectiveness of each component of RABERT in further analysi |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3459637.3482165 | Conference on Information and Knowledge Management |
DocType | Citations | PageRank |
Conference | 1 | 0.40 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taegwan Kang | 1 | 1 | 1.07 |
| Minwoo Lee | 2 | 1 | 0.40 |
| Nakyeong Yang | 3 | 1 | 0.40 |
| Kyomin Jung | 4 | 394 | 37.38 |