Discovering possible candidate elements and capturing their connections to form the output triplets constitute the core challenge of aspect sentiment triplet extraction (ASTE). However, the information encapsulated by prevailing methods within a solitary sentence may often prove insufficient, particularly in complex scenarios characterized by uncommon aspect, opinion terms or intricate syntax patterns. To mitigate these limitations, we advocate incorporating inter-sentence information retrieval to enrich intra-sentence representations within ASTE. One existing study has proposed a method dubbed Retrieval-Based Aspect Sentiment Triplet Extraction via Label Interpolation (RLI), which retrieves triplets from the corpus to augment the representations of a candidate aspect-opinion pair and further improve sentiment prediction. Nevertheless, obtaining data with standard triplets might be challenging in practice. Therefore, we propose an approach, namely Multi-Task ASTE with the Corpus-Enhanced Graph (MACG), to conduct sentence-level retrieval and extract helpful information from unlabeled similar sentences. Specifically, we design a corpus-level enhanced graph to capture inter-sentence information, alongside a local graph preserving intra-sentence information. A graph neural network is subsequently employed to adaptively learn enhanced representations of the target sentence for ASTE. RLI and MACG collaboratively form a comprehensive methodological framework, which is effective in both scenarios with and without standard triplet labels. Extensive experiments on two benchmarks demonstrate the superiority and flexibility of retrieving inter-sentence information, which underscores their potential to advance ASTE by leveraging neighboring information.

Knowledge graphs are essential tools for representing real-world facts and finding wide applications in various domains. However, the process of constructing knowledge graphs often introduces noises and errors, which can negatively impact the performance of downstream applications. Current methods for knowledge graph error detection primarily focus on graph structure and overlook the importance of textual information in error detection. Therefore, this paper proposes a novel error detection framework that combines both structural and textual information. The framework utilizes a confidence module for error detection while generating knowledge embeddings. The performance of this approach outperforms baseline methods in error detection and link prediction experiments, particularly achieving state-of-the-art performance in the error detection task.

Transfer learning has attracted a large amount of interest and research in last decades, and some effort has been made to build more precise recommendation systems. Most previous transfer recommendation systems assume that the target domain shares the same/similar rating patterns with the auxiliary source domain, which is used to improve the recommendation performance. However, almost all existing transfer learning work does not consider the characteristics of sequential data. In this paper, we study the new cross-domain recommendation scenario by mining novelty-seeking trait. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. Previous work performed on only one single target domain may not fully characterize users’ novelty-seeking trait well due to the data scarcity and sparsity, leading to the poor recommendation performance. Along this line, we propose a new cross-domain novelty-seeking trait mining model (CDNST for short) to improve the sequential recommendation performance by transferring the knowledge from auxiliary source domain. We conduct systematic experiments on three domain datasets crawled from Douban to demonstrate the effectiveness of our proposed model. Moreover, we analyze the directed influence of the temporal property at the source and target domains in detail.