{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:38:10Z","timestamp":1723016290269},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Various types of target information have been considered in aspect-based sentiment analysis, such as entities and aspects. Existing research has realized the importance of targets and developed methods with the goal of precisely modeling their contexts via generating target-specific representations. However, all these methods ignore that these representations cannot be learned well due to the lack of sufficient human-annotated target-related reviews, which leads to the data sparsity challenge, a.k.a. cold-start problem here. In this paper, we focus on a more general multiple entity aspect-based sentiment analysis (ME-ABSA) task which aims at identifying the sentiment polarity of different aspects of multiple entities in their context. Faced with severe cold-start scenario, we develop a novel and extensible deep memory network framework with cold-start aware computational layers which use frequency-guided attention mechanism to accentuate on the most related targets, and then compose their representations into a complementary vector for enhancing the representations of cold-start entities and aspects. To verify the effectiveness of the framework, we instantiate it with a concrete context encoding method and then apply the model to the ME-ABSA task. Experimental results conducted on two public datasets demonstrate that the proposed approach outperforms state-of-the-art baselines on ME-ABSA task.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/722","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"5197-5203","source":"Crossref","is-referenced-by-count":3,"title":["Cold-Start Aware Deep Memory Network for Multi-Entity Aspect-Based Sentiment Analysis"],"prefix":"10.24963","author":[{"given":"Kaisong","family":"Song","sequence":"first","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Gao","sequence":"additional","affiliation":[{"name":"Victoria University of Wellington, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lujun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Lin","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changlong","family":"Sun","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaozhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Indiana University Bloomington, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:51:21Z","timestamp":1564300281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/722"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/722","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}