CCF自然语言处理与中文计算国际会议（NLPCC）是中国计算机联合会中文信息技术委员会（CCF-TCCI）的年会。NLPCC是一个在自然语言处理（NLP）和中文计算（CC）领域领先的国际会议。它是学术界、工业界和政府的研究人员和实践者分享他们的想法、研究成果和经验，并促进他们在该领域的研究和技术创新的主要论坛。官网链接：http://tcci.ccf.org.cn/conference/2019/
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如今，网络越来越大，越来越复杂，应用越来越广泛。众所周知，网络数据是复杂和具有挑战性的。要有效地处理图数据，第一个关键的挑战是网络数据表示，即如何正确地表示网络，使模式发现、分析和预测等高级分析任务在时间和空间上都能有效地进行。在这次演讲中，我将介绍网络嵌入和GCN的最新发展趋势和最新进展，包括解纠缠GCN、抗攻击GCN以及用于网络嵌入的自动机器学习。
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【142页ppt】Frontiers in GNN and Network Embedding
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Aspect-based sentiment analysis (ABSA) aims at analyzing the sentiment of a given aspect in a sentence. Recently, neural network-based methods have achieved promising results in existing ABSA datasets. However, these datasets tend to degenerate to sentence-level sentiment analysis because most sentences contain only one aspect or multiple aspects with the same sentiment polarity. To facilitate the research of ABSA, NLPCC 2020 Shared Task 2 releases a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset. In the MAMS dataset, each sentence contains at least two different aspects with different sentiment polarities, which makes ABSA more complex and challenging. To address the challenging dataset, we re-formalize ABSA as a problem of multi-aspect sentiment analysis, and propose a novel Transformer-based Multi-aspect Modeling scheme (TMM), which can capture potential relations between multiple aspects and simultaneously detect the sentiment of all aspects in a sentence. Experiment results on the MAMS dataset show that our method achieves noticeable improvements compared with strong baselines such as BERT and RoBERTa, and finally ranks the 2nd in NLPCC 2020 Shared Task 2 Evaluation.
